#5.모델기반_협업필터링_Matrix_Factorization.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/moch1996/Vaycold/blob/main/Recommend_Algorithm/%EB%AA%A8%EB%8D%B8%EA%B8%B0%EB%B0%98_%ED%98%91%EC%97%85%ED%95%84%ED%84%B0%EB%A7%81%2C_Matrix_Factorization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzB5pzulAPWe"
      },
      "source": [
        "# Matrix Factorization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H090RnUjAYU2"
      },
      "source": [
        "## 라이브러리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eo7-iAVBvZ8l"
      },
      "source": [
        "import os\r\n",
        "from math import sqrt\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "from tqdm import tqdm_notebook as tqdm\r\n",
        "from google.colab import drive\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNDeP3B-vu7J",
        "outputId": "0016af0e-79ce-4703-fd64-b232daaec318"
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPDecrlZw2Ul"
      },
      "source": [
        "## 데이터 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsAJRTmvvy9J"
      },
      "source": [
        "path = '/content/drive/MyDrive/fastcampus/recommend/movielens'\r\n",
        "ratings_df = pd.read_csv(os.path.join(path, 'ratings.csv'), encoding='utf-8')\r\n",
        "movies_df = pd.read_csv(os.path.join(path, 'movies.csv'), index_col='movieId', encoding='utf-8')\r\n",
        "tags_df = pd.read_csv(os.path.join(path, 'tags.csv'), encoding='utf-8')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoJfgaafA2kL",
        "outputId": "40df5a8f-f16b-458f-d1e3-51511607ded8"
      },
      "source": [
        "train_df , test_df = train_test_split(ratings_df, test_size = 0.2, random_state=1234)\r\n",
        "\r\n",
        "print(train_df.shape)\r\n",
        "print(test_df.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(80668, 4)\n",
            "(20168, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "Vy36SesSBnFv",
        "outputId": "7bafa4a5-1fdf-4827-d98a-80e2f320e411"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>95713</th>\n",
              "      <td>600</td>\n",
              "      <td>5943</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1237714356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61560</th>\n",
              "      <td>407</td>\n",
              "      <td>2571</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1424349171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77204</th>\n",
              "      <td>482</td>\n",
              "      <td>8958</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1105397126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93367</th>\n",
              "      <td>599</td>\n",
              "      <td>2322</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1498515283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90892</th>\n",
              "      <td>590</td>\n",
              "      <td>2959</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1258416553</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       userId  movieId  rating   timestamp\n",
              "95713     600     5943     3.0  1237714356\n",
              "61560     407     2571     5.0  1424349171\n",
              "77204     482     8958     4.0  1105397126\n",
              "93367     599     2322     2.5  1498515283\n",
              "90892     590     2959     3.5  1258416553"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "DLMebDjfBGr9",
        "outputId": "f0a2d19e-476a-4994-e85c-687c804fc49e"
      },
      "source": [
        "# Sparse Matrix 만들기 \r\n",
        "# 만드는 이유 : latent matrix를 만들기 위함 \r\n",
        "\r\n",
        "sparse_matrix = train_df.groupby('movieId').apply(lambda x : pd.Series(x['rating'].values, index =x['userId'])).unstack()\r\n",
        "sparse_matrix"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>userId</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
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              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>...</th>\n",
              "      <th>571</th>\n",
              "      <th>572</th>\n",
              "      <th>573</th>\n",
              "      <th>574</th>\n",
              "      <th>575</th>\n",
              "      <th>576</th>\n",
              "      <th>577</th>\n",
              "      <th>578</th>\n",
              "      <th>579</th>\n",
              "      <th>580</th>\n",
              "      <th>581</th>\n",
              "      <th>582</th>\n",
              "      <th>583</th>\n",
              "      <th>584</th>\n",
              "      <th>585</th>\n",
              "      <th>586</th>\n",
              "      <th>587</th>\n",
              "      <th>588</th>\n",
              "      <th>589</th>\n",
              "      <th>590</th>\n",
              "      <th>591</th>\n",
              "      <th>592</th>\n",
              "      <th>593</th>\n",
              "      <th>594</th>\n",
              "      <th>595</th>\n",
              "      <th>596</th>\n",
              "      <th>597</th>\n",
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              "      <th>609</th>\n",
              "      <th>610</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193573</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193579</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193581</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193587</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193609</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8938 rows × 610 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "userId   1    2    3    4    5    6    7    ...  604  605  606  607  608  609  610\n",
              "movieId                                     ...                                   \n",
              "1        NaN  NaN  NaN  NaN  4.0  NaN  4.5  ...  3.0  4.0  2.5  NaN  2.5  NaN  5.0\n",
              "2        NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  3.5  NaN  NaN  2.0  NaN  NaN\n",
              "3        4.0  NaN  NaN  NaN  NaN  5.0  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "4        NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "5        NaN  NaN  NaN  NaN  NaN  5.0  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
              "193573   NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "193579   NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "193581   NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "193587   NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "193609   NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "\n",
              "[8938 rows x 610 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-76_7HDCK-8"
      },
      "source": [
        "# 결측값이 존재할 수 밖에 없음 \r\n",
        "# 이 값들을 무언가로 채워줘야 됨. \r\n",
        "# 무얼로 채워야 될까??\r\n",
        "# 모델기반 협업필터링도 유저기반인지 아이템 기반인지 살펴봐야 할 듯\r\n",
        "# 그래서 유저기반으로 할 때는 결측값을 유저의 평균치로 넣어주고\r\n",
        "# 아이템 기반일 때는 아이템의 평균치로 결측값을 채우는 방향으로 잡았다."
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YnInflPC1RE"
      },
      "source": [
        "# 유저기반 sparse_matrix\r\n",
        "sparse_matrix_withuser = sparse_matrix.apply(lambda x : x.fillna(x.mean()), axis=0)\r\n",
        "\r\n",
        "# 아이템 기반 sparse_matrix\r\n",
        "sparse_matrix_withmovie = sparse_matrix.apply(lambda x : x.fillna(x.mean()), axis=1)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "6U6BMpwJDQy8",
        "outputId": "54df94dc-92f2-4872-a237-80bad26a6e75"
      },
      "source": [
        "sparse_matrix_withmovie.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>userId</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>...</th>\n",
              "      <th>571</th>\n",
              "      <th>572</th>\n",
              "      <th>573</th>\n",
              "      <th>574</th>\n",
              "      <th>575</th>\n",
              "      <th>576</th>\n",
              "      <th>577</th>\n",
              "      <th>578</th>\n",
              "      <th>579</th>\n",
              "      <th>580</th>\n",
              "      <th>581</th>\n",
              "      <th>582</th>\n",
              "      <th>583</th>\n",
              "      <th>584</th>\n",
              "      <th>585</th>\n",
              "      <th>586</th>\n",
              "      <th>587</th>\n",
              "      <th>588</th>\n",
              "      <th>589</th>\n",
              "      <th>590</th>\n",
              "      <th>591</th>\n",
              "      <th>592</th>\n",
              "      <th>593</th>\n",
              "      <th>594</th>\n",
              "      <th>595</th>\n",
              "      <th>596</th>\n",
              "      <th>597</th>\n",
              "      <th>598</th>\n",
              "      <th>599</th>\n",
              "      <th>600</th>\n",
              "      <th>601</th>\n",
              "      <th>602</th>\n",
              "      <th>603</th>\n",
              "      <th>604</th>\n",
              "      <th>605</th>\n",
              "      <th>606</th>\n",
              "      <th>607</th>\n",
              "      <th>608</th>\n",
              "      <th>609</th>\n",
              "      <th>610</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>...</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.893491</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>...</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>2.50</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.396341</td>\n",
              "      <td>3.396341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>...</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "      <td>3.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>...</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>...</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "      <td>3.039474</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 610 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "userId        1         2         3    ...       608       609       610\n",
              "movieId                                ...                              \n",
              "1        3.893491  3.893491  3.893491  ...  2.500000  3.893491  5.000000\n",
              "2        3.396341  3.396341  3.396341  ...  2.000000  3.396341  3.396341\n",
              "3        4.000000  3.454545  3.454545  ...  3.454545  3.454545  3.454545\n",
              "4        2.250000  2.250000  2.250000  ...  2.250000  2.250000  2.250000\n",
              "5        3.039474  3.039474  3.039474  ...  3.039474  3.039474  3.039474\n",
              "\n",
              "[5 rows x 610 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "3DOliM5pDXS1",
        "outputId": "7eaf9277-2032-4a41-bcbb-2b168c137f5b"
      },
      "source": [
        "sparse_matrix_withuser.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>userId</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>...</th>\n",
              "      <th>571</th>\n",
              "      <th>572</th>\n",
              "      <th>573</th>\n",
              "      <th>574</th>\n",
              "      <th>575</th>\n",
              "      <th>576</th>\n",
              "      <th>577</th>\n",
              "      <th>578</th>\n",
              "      <th>579</th>\n",
              "      <th>580</th>\n",
              "      <th>581</th>\n",
              "      <th>582</th>\n",
              "      <th>583</th>\n",
              "      <th>584</th>\n",
              "      <th>585</th>\n",
              "      <th>586</th>\n",
              "      <th>587</th>\n",
              "      <th>588</th>\n",
              "      <th>589</th>\n",
              "      <th>590</th>\n",
              "      <th>591</th>\n",
              "      <th>592</th>\n",
              "      <th>593</th>\n",
              "      <th>594</th>\n",
              "      <th>595</th>\n",
              "      <th>596</th>\n",
              "      <th>597</th>\n",
              "      <th>598</th>\n",
              "      <th>599</th>\n",
              "      <th>600</th>\n",
              "      <th>601</th>\n",
              "      <th>602</th>\n",
              "      <th>603</th>\n",
              "      <th>604</th>\n",
              "      <th>605</th>\n",
              "      <th>606</th>\n",
              "      <th>607</th>\n",
              "      <th>608</th>\n",
              "      <th>609</th>\n",
              "      <th>610</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.320652</td>\n",
              "      <td>3.94</td>\n",
              "      <td>2.516667</td>\n",
              "      <td>3.631868</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.504132</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>3.486486</td>\n",
              "      <td>3.1</td>\n",
              "      <td>3.292035</td>\n",
              "      <td>3.758621</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>3.56</td>\n",
              "      <td>3.45</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.723684</td>\n",
              "      <td>4.195402</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.60396</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.618557</td>\n",
              "      <td>3.659341</td>\n",
              "      <td>4.785714</td>\n",
              "      <td>3.3125</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.00547</td>\n",
              "      <td>4.19697</td>\n",
              "      <td>4.711538</td>\n",
              "      <td>3.911765</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.330645</td>\n",
              "      <td>4.052632</td>\n",
              "      <td>2.609756</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.111111</td>\n",
              "      <td>3.97561</td>\n",
              "      <td>3.837209</td>\n",
              "      <td>...</td>\n",
              "      <td>2.536585</td>\n",
              "      <td>4.152</td>\n",
              "      <td>4.192308</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.033333</td>\n",
              "      <td>2.916667</td>\n",
              "      <td>3.589147</td>\n",
              "      <td>3.934783</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.534286</td>\n",
              "      <td>4.370968</td>\n",
              "      <td>4.054348</td>\n",
              "      <td>3.255556</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.339623</td>\n",
              "      <td>4.328025</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.217391</td>\n",
              "      <td>3.878788</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.26087</td>\n",
              "      <td>3.653333</td>\n",
              "      <td>3.23494</td>\n",
              "      <td>3.860963</td>\n",
              "      <td>4.210526</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.980337</td>\n",
              "      <td>3.764706</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.991468</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.473684</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.772414</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.275862</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.320652</td>\n",
              "      <td>3.94</td>\n",
              "      <td>2.516667</td>\n",
              "      <td>3.631868</td>\n",
              "      <td>3.636364</td>\n",
              "      <td>3.504132</td>\n",
              "      <td>3.247967</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.1</td>\n",
              "      <td>3.292035</td>\n",
              "      <td>3.758621</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>3.56</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.422018</td>\n",
              "      <td>3.723684</td>\n",
              "      <td>4.195402</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.618557</td>\n",
              "      <td>3.659341</td>\n",
              "      <td>4.785714</td>\n",
              "      <td>3.3125</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.00547</td>\n",
              "      <td>4.19697</td>\n",
              "      <td>4.711538</td>\n",
              "      <td>3.911765</td>\n",
              "      <td>3.759036</td>\n",
              "      <td>3.851562</td>\n",
              "      <td>3.330645</td>\n",
              "      <td>4.052632</td>\n",
              "      <td>2.609756</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.111111</td>\n",
              "      <td>3.97561</td>\n",
              "      <td>3.837209</td>\n",
              "      <td>...</td>\n",
              "      <td>2.536585</td>\n",
              "      <td>4.152</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.033333</td>\n",
              "      <td>2.916667</td>\n",
              "      <td>3.589147</td>\n",
              "      <td>3.934783</td>\n",
              "      <td>3.921875</td>\n",
              "      <td>3.534286</td>\n",
              "      <td>4.370968</td>\n",
              "      <td>4.054348</td>\n",
              "      <td>3.255556</td>\n",
              "      <td>4.015385</td>\n",
              "      <td>4.339623</td>\n",
              "      <td>4.328025</td>\n",
              "      <td>3.992857</td>\n",
              "      <td>3.217391</td>\n",
              "      <td>3.878788</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.26087</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.23494</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.210526</td>\n",
              "      <td>3.507622</td>\n",
              "      <td>3.980337</td>\n",
              "      <td>3.764706</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.396104</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.494024</td>\n",
              "      <td>3.486842</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>3.649718</td>\n",
              "      <td>3.772414</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.275862</td>\n",
              "      <td>3.678709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.94</td>\n",
              "      <td>2.516667</td>\n",
              "      <td>3.631868</td>\n",
              "      <td>3.636364</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.247967</td>\n",
              "      <td>3.486486</td>\n",
              "      <td>3.1</td>\n",
              "      <td>3.292035</td>\n",
              "      <td>3.758621</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>3.56</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.422018</td>\n",
              "      <td>3.723684</td>\n",
              "      <td>4.195402</td>\n",
              "      <td>3.741463</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.60396</td>\n",
              "      <td>3.268056</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.618557</td>\n",
              "      <td>3.659341</td>\n",
              "      <td>4.785714</td>\n",
              "      <td>3.3125</td>\n",
              "      <td>3.530973</td>\n",
              "      <td>3.00547</td>\n",
              "      <td>4.19697</td>\n",
              "      <td>4.711538</td>\n",
              "      <td>3.911765</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.851562</td>\n",
              "      <td>3.330645</td>\n",
              "      <td>4.052632</td>\n",
              "      <td>2.609756</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.111111</td>\n",
              "      <td>3.97561</td>\n",
              "      <td>3.837209</td>\n",
              "      <td>...</td>\n",
              "      <td>2.536585</td>\n",
              "      <td>4.152</td>\n",
              "      <td>4.192308</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.033333</td>\n",
              "      <td>2.916667</td>\n",
              "      <td>3.589147</td>\n",
              "      <td>3.934783</td>\n",
              "      <td>3.921875</td>\n",
              "      <td>3.534286</td>\n",
              "      <td>4.370968</td>\n",
              "      <td>4.054348</td>\n",
              "      <td>3.255556</td>\n",
              "      <td>4.015385</td>\n",
              "      <td>4.339623</td>\n",
              "      <td>4.328025</td>\n",
              "      <td>3.992857</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.878788</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.26087</td>\n",
              "      <td>3.653333</td>\n",
              "      <td>3.23494</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.210526</td>\n",
              "      <td>3.507622</td>\n",
              "      <td>3.980337</td>\n",
              "      <td>3.764706</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.991468</td>\n",
              "      <td>4.396104</td>\n",
              "      <td>3.473684</td>\n",
              "      <td>3.494024</td>\n",
              "      <td>3.486842</td>\n",
              "      <td>3.255618</td>\n",
              "      <td>3.649718</td>\n",
              "      <td>3.772414</td>\n",
              "      <td>3.145865</td>\n",
              "      <td>3.275862</td>\n",
              "      <td>3.678709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.320652</td>\n",
              "      <td>3.94</td>\n",
              "      <td>2.516667</td>\n",
              "      <td>3.631868</td>\n",
              "      <td>3.636364</td>\n",
              "      <td>3.504132</td>\n",
              "      <td>3.247967</td>\n",
              "      <td>3.486486</td>\n",
              "      <td>3.1</td>\n",
              "      <td>3.292035</td>\n",
              "      <td>3.758621</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>3.56</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.422018</td>\n",
              "      <td>3.723684</td>\n",
              "      <td>4.195402</td>\n",
              "      <td>3.741463</td>\n",
              "      <td>2.646341</td>\n",
              "      <td>3.60396</td>\n",
              "      <td>3.268056</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.618557</td>\n",
              "      <td>3.659341</td>\n",
              "      <td>4.785714</td>\n",
              "      <td>3.3125</td>\n",
              "      <td>3.530973</td>\n",
              "      <td>3.00547</td>\n",
              "      <td>4.19697</td>\n",
              "      <td>4.711538</td>\n",
              "      <td>3.911765</td>\n",
              "      <td>3.759036</td>\n",
              "      <td>3.851562</td>\n",
              "      <td>3.330645</td>\n",
              "      <td>4.052632</td>\n",
              "      <td>2.609756</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.111111</td>\n",
              "      <td>3.97561</td>\n",
              "      <td>3.837209</td>\n",
              "      <td>...</td>\n",
              "      <td>2.536585</td>\n",
              "      <td>4.152</td>\n",
              "      <td>4.192308</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.033333</td>\n",
              "      <td>2.916667</td>\n",
              "      <td>3.589147</td>\n",
              "      <td>3.934783</td>\n",
              "      <td>3.921875</td>\n",
              "      <td>3.534286</td>\n",
              "      <td>4.370968</td>\n",
              "      <td>4.054348</td>\n",
              "      <td>3.255556</td>\n",
              "      <td>4.015385</td>\n",
              "      <td>4.339623</td>\n",
              "      <td>4.328025</td>\n",
              "      <td>3.992857</td>\n",
              "      <td>3.217391</td>\n",
              "      <td>3.878788</td>\n",
              "      <td>3.361552</td>\n",
              "      <td>3.26087</td>\n",
              "      <td>3.653333</td>\n",
              "      <td>3.23494</td>\n",
              "      <td>3.860963</td>\n",
              "      <td>4.210526</td>\n",
              "      <td>3.507622</td>\n",
              "      <td>3.980337</td>\n",
              "      <td>3.764706</td>\n",
              "      <td>2.647059</td>\n",
              "      <td>2.991468</td>\n",
              "      <td>4.396104</td>\n",
              "      <td>3.473684</td>\n",
              "      <td>3.494024</td>\n",
              "      <td>3.486842</td>\n",
              "      <td>3.255618</td>\n",
              "      <td>3.649718</td>\n",
              "      <td>3.772414</td>\n",
              "      <td>3.145865</td>\n",
              "      <td>3.275862</td>\n",
              "      <td>3.678709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.320652</td>\n",
              "      <td>3.94</td>\n",
              "      <td>2.516667</td>\n",
              "      <td>3.631868</td>\n",
              "      <td>3.636364</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.247967</td>\n",
              "      <td>3.486486</td>\n",
              "      <td>3.1</td>\n",
              "      <td>3.292035</td>\n",
              "      <td>3.758621</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>3.56</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.422018</td>\n",
              "      <td>3.723684</td>\n",
              "      <td>4.195402</td>\n",
              "      <td>3.741463</td>\n",
              "      <td>2.646341</td>\n",
              "      <td>3.60396</td>\n",
              "      <td>3.268056</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.618557</td>\n",
              "      <td>3.659341</td>\n",
              "      <td>4.785714</td>\n",
              "      <td>3.3125</td>\n",
              "      <td>3.530973</td>\n",
              "      <td>3.00547</td>\n",
              "      <td>4.19697</td>\n",
              "      <td>4.711538</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.759036</td>\n",
              "      <td>3.851562</td>\n",
              "      <td>3.330645</td>\n",
              "      <td>4.052632</td>\n",
              "      <td>2.609756</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.111111</td>\n",
              "      <td>3.97561</td>\n",
              "      <td>3.837209</td>\n",
              "      <td>...</td>\n",
              "      <td>2.536585</td>\n",
              "      <td>4.152</td>\n",
              "      <td>4.192308</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.033333</td>\n",
              "      <td>2.916667</td>\n",
              "      <td>3.589147</td>\n",
              "      <td>3.934783</td>\n",
              "      <td>3.921875</td>\n",
              "      <td>3.534286</td>\n",
              "      <td>4.370968</td>\n",
              "      <td>4.054348</td>\n",
              "      <td>3.255556</td>\n",
              "      <td>4.015385</td>\n",
              "      <td>4.339623</td>\n",
              "      <td>4.328025</td>\n",
              "      <td>3.992857</td>\n",
              "      <td>3.217391</td>\n",
              "      <td>3.878788</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.26087</td>\n",
              "      <td>3.653333</td>\n",
              "      <td>3.23494</td>\n",
              "      <td>3.860963</td>\n",
              "      <td>4.210526</td>\n",
              "      <td>3.507622</td>\n",
              "      <td>3.980337</td>\n",
              "      <td>3.764706</td>\n",
              "      <td>2.647059</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>4.396104</td>\n",
              "      <td>3.473684</td>\n",
              "      <td>3.494024</td>\n",
              "      <td>3.486842</td>\n",
              "      <td>3.255618</td>\n",
              "      <td>3.649718</td>\n",
              "      <td>3.772414</td>\n",
              "      <td>3.145865</td>\n",
              "      <td>3.275862</td>\n",
              "      <td>3.678709</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 610 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "userId        1     2         3    ...       608       609       610\n",
              "movieId                            ...                              \n",
              "1        4.320652  3.94  2.516667  ...  2.500000  3.275862  5.000000\n",
              "2        4.320652  3.94  2.516667  ...  2.000000  3.275862  3.678709\n",
              "3        4.000000  3.94  2.516667  ...  3.145865  3.275862  3.678709\n",
              "4        4.320652  3.94  2.516667  ...  3.145865  3.275862  3.678709\n",
              "5        4.320652  3.94  2.516667  ...  3.145865  3.275862  3.678709\n",
              "\n",
              "[5 rows x 610 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjIhqDoQDfNu"
      },
      "source": [
        "## Matrix Factorization with SVD "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kan-isJPDZ76"
      },
      "source": [
        "# numpy의 linalg 메소드의 svd 메소드가 있음.\r\n",
        "# np.linalg.svd = u, s, vh 가 나오게 되는데 \r\n",
        "# 여기서 s는 고유값의 1차원 배열임 trace(s) 같은 느낌\r\n",
        "# u와 vh는 레프트, 라이트임\r\n",
        "\r\n",
        "def get_svd(s_matrix, k=300):\r\n",
        "  u, s, vh = np.linalg.svd(s_matrix.transpose())\r\n",
        "  S = s[:k] * np.identity(k, np.float)\r\n",
        "  T = u[:,:k]\r\n",
        "  Dt = vh[:k,:]\r\n",
        "\r\n",
        "  item_factors = np.transpose(np.matmul(S, Dt))\r\n",
        "  user_factors = np.transpose(T)\r\n",
        "\r\n",
        "  return item_factors, user_factors"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNGRTiSNIPBH"
      },
      "source": [
        "# movie rating 기준"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfy4Yg9PG7JX"
      },
      "source": [
        "item_factors, user_factors = get_svd(sparse_matrix_withmovie)\r\n",
        "prediction_result_df = pd.DataFrame(np.matmul(item_factors, user_factors),\r\n",
        "                                    columns=sparse_matrix_withmovie.columns.values, index=sparse_matrix_withmovie.index.values)\r\n",
        "\r\n",
        "movie_prediction_result_df = prediction_result_df.transpose()"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldym2QTEHJBY",
        "outputId": "3f22b47f-173a-4fac-e8b8-e194c14cc79e"
      },
      "source": [
        "print(item_factors.shape)\r\n",
        "print(user_factors.shape)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8938, 300)\n",
            "(300, 610)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "aUu1C5-JHVjL",
        "outputId": "68e9d798-9493-4b7a-fdd8-e3acad147a7a"
      },
      "source": [
        "movie_prediction_result_df.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>34</th>\n",
              "      <th>36</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>...</th>\n",
              "      <th>184257</th>\n",
              "      <th>184349</th>\n",
              "      <th>184471</th>\n",
              "      <th>184721</th>\n",
              "      <th>184791</th>\n",
              "      <th>184931</th>\n",
              "      <th>184987</th>\n",
              "      <th>184997</th>\n",
              "      <th>185033</th>\n",
              "      <th>185135</th>\n",
              "      <th>185435</th>\n",
              "      <th>185585</th>\n",
              "      <th>187031</th>\n",
              "      <th>187541</th>\n",
              "      <th>187593</th>\n",
              "      <th>187595</th>\n",
              "      <th>187717</th>\n",
              "      <th>188189</th>\n",
              "      <th>188301</th>\n",
              "      <th>188675</th>\n",
              "      <th>188797</th>\n",
              "      <th>189043</th>\n",
              "      <th>189111</th>\n",
              "      <th>189333</th>\n",
              "      <th>189547</th>\n",
              "      <th>190183</th>\n",
              "      <th>190207</th>\n",
              "      <th>190209</th>\n",
              "      <th>190213</th>\n",
              "      <th>190215</th>\n",
              "      <th>190219</th>\n",
              "      <th>191005</th>\n",
              "      <th>193565</th>\n",
              "      <th>193567</th>\n",
              "      <th>193571</th>\n",
              "      <th>193573</th>\n",
              "      <th>193579</th>\n",
              "      <th>193581</th>\n",
              "      <th>193587</th>\n",
              "      <th>193609</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.908820</td>\n",
              "      <td>3.390957</td>\n",
              "      <td>4.035030</td>\n",
              "      <td>2.252912</td>\n",
              "      <td>3.053903</td>\n",
              "      <td>3.981066</td>\n",
              "      <td>3.183727</td>\n",
              "      <td>2.744891</td>\n",
              "      <td>2.909088</td>\n",
              "      <td>3.505548</td>\n",
              "      <td>3.706015</td>\n",
              "      <td>2.374035</td>\n",
              "      <td>3.123550</td>\n",
              "      <td>3.933145</td>\n",
              "      <td>2.971819</td>\n",
              "      <td>3.911682</td>\n",
              "      <td>3.793296</td>\n",
              "      <td>3.616735</td>\n",
              "      <td>2.723460</td>\n",
              "      <td>2.714942</td>\n",
              "      <td>3.467551</td>\n",
              "      <td>3.257282</td>\n",
              "      <td>3.125723</td>\n",
              "      <td>3.116005</td>\n",
              "      <td>3.666359</td>\n",
              "      <td>3.445724</td>\n",
              "      <td>3.481224</td>\n",
              "      <td>4.057030</td>\n",
              "      <td>3.950146</td>\n",
              "      <td>3.003699</td>\n",
              "      <td>3.127470</td>\n",
              "      <td>4.045563</td>\n",
              "      <td>3.725531</td>\n",
              "      <td>3.732041</td>\n",
              "      <td>2.000468</td>\n",
              "      <td>3.322564</td>\n",
              "      <td>3.499942</td>\n",
              "      <td>3.799698</td>\n",
              "      <td>3.176480</td>\n",
              "      <td>3.187039</td>\n",
              "      <td>...</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>3.499942</td>\n",
              "      <td>2.498336</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>3.249974</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>2.999950</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>1.999967</td>\n",
              "      <td>4.749644</td>\n",
              "      <td>2.999950</td>\n",
              "      <td>2.750140</td>\n",
              "      <td>3.251965</td>\n",
              "      <td>3.505465</td>\n",
              "      <td>4.126688</td>\n",
              "      <td>3.501081</td>\n",
              "      <td>4.999917</td>\n",
              "      <td>4.499925</td>\n",
              "      <td>3.500506</td>\n",
              "      <td>3.499942</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>2.499959</td>\n",
              "      <td>2.999950</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>0.999983</td>\n",
              "      <td>3.499942</td>\n",
              "      <td>1.499975</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>0.999983</td>\n",
              "      <td>1.499975</td>\n",
              "      <td>0.999983</td>\n",
              "      <td>4.499925</td>\n",
              "      <td>3.499942</td>\n",
              "      <td>2.999950</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>3.499942</td>\n",
              "      <td>3.999934</td>\n",
              "      <td>3.499942</td>\n",
              "      <td>3.999934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.911434</td>\n",
              "      <td>3.445683</td>\n",
              "      <td>3.461056</td>\n",
              "      <td>2.240729</td>\n",
              "      <td>2.998555</td>\n",
              "      <td>3.959505</td>\n",
              "      <td>3.151831</td>\n",
              "      <td>2.751379</td>\n",
              "      <td>2.910568</td>\n",
              "      <td>3.406105</td>\n",
              "      <td>3.706765</td>\n",
              "      <td>2.358869</td>\n",
              "      <td>3.126824</td>\n",
              "      <td>3.946110</td>\n",
              "      <td>3.016277</td>\n",
              "      <td>3.840996</td>\n",
              "      <td>3.829437</td>\n",
              "      <td>3.635910</td>\n",
              "      <td>2.722463</td>\n",
              "      <td>2.741404</td>\n",
              "      <td>3.493050</td>\n",
              "      <td>3.267066</td>\n",
              "      <td>3.185609</td>\n",
              "      <td>3.081252</td>\n",
              "      <td>3.647671</td>\n",
              "      <td>3.423465</td>\n",
              "      <td>3.500961</td>\n",
              "      <td>4.075291</td>\n",
              "      <td>3.973008</td>\n",
              "      <td>3.001640</td>\n",
              "      <td>3.126769</td>\n",
              "      <td>3.953079</td>\n",
              "      <td>3.763999</td>\n",
              "      <td>3.739251</td>\n",
              "      <td>2.000506</td>\n",
              "      <td>3.327100</td>\n",
              "      <td>3.499797</td>\n",
              "      <td>3.803588</td>\n",
              "      <td>3.171343</td>\n",
              "      <td>3.168945</td>\n",
              "      <td>...</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>3.499797</td>\n",
              "      <td>2.494128</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>3.255965</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>2.999826</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>1.999884</td>\n",
              "      <td>4.757657</td>\n",
              "      <td>2.999826</td>\n",
              "      <td>2.749987</td>\n",
              "      <td>3.251145</td>\n",
              "      <td>3.489683</td>\n",
              "      <td>4.160032</td>\n",
              "      <td>3.494873</td>\n",
              "      <td>4.999710</td>\n",
              "      <td>4.499739</td>\n",
              "      <td>3.505032</td>\n",
              "      <td>3.499797</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>2.499855</td>\n",
              "      <td>2.999826</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>0.999942</td>\n",
              "      <td>3.499797</td>\n",
              "      <td>1.499913</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>0.999942</td>\n",
              "      <td>1.499913</td>\n",
              "      <td>0.999942</td>\n",
              "      <td>4.499739</td>\n",
              "      <td>3.499797</td>\n",
              "      <td>2.999826</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>3.499797</td>\n",
              "      <td>3.999768</td>\n",
              "      <td>3.499797</td>\n",
              "      <td>3.999768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.896327</td>\n",
              "      <td>3.383747</td>\n",
              "      <td>3.439498</td>\n",
              "      <td>2.240612</td>\n",
              "      <td>3.049043</td>\n",
              "      <td>3.935766</td>\n",
              "      <td>3.174809</td>\n",
              "      <td>2.738581</td>\n",
              "      <td>2.910505</td>\n",
              "      <td>3.515399</td>\n",
              "      <td>3.705274</td>\n",
              "      <td>2.346706</td>\n",
              "      <td>3.117167</td>\n",
              "      <td>3.950714</td>\n",
              "      <td>3.000556</td>\n",
              "      <td>3.900426</td>\n",
              "      <td>3.825145</td>\n",
              "      <td>3.602151</td>\n",
              "      <td>2.735374</td>\n",
              "      <td>2.730128</td>\n",
              "      <td>3.429712</td>\n",
              "      <td>3.272879</td>\n",
              "      <td>3.139803</td>\n",
              "      <td>3.148035</td>\n",
              "      <td>3.701209</td>\n",
              "      <td>3.462291</td>\n",
              "      <td>3.481890</td>\n",
              "      <td>4.047783</td>\n",
              "      <td>3.947432</td>\n",
              "      <td>2.993830</td>\n",
              "      <td>0.449036</td>\n",
              "      <td>3.979410</td>\n",
              "      <td>3.709393</td>\n",
              "      <td>3.738304</td>\n",
              "      <td>2.001326</td>\n",
              "      <td>3.303376</td>\n",
              "      <td>3.500003</td>\n",
              "      <td>3.810915</td>\n",
              "      <td>3.161767</td>\n",
              "      <td>3.195110</td>\n",
              "      <td>...</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>3.500003</td>\n",
              "      <td>2.495915</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>3.249723</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>3.000003</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>2.000002</td>\n",
              "      <td>4.749722</td>\n",
              "      <td>3.000003</td>\n",
              "      <td>2.749922</td>\n",
              "      <td>3.254521</td>\n",
              "      <td>3.505089</td>\n",
              "      <td>4.148535</td>\n",
              "      <td>3.498766</td>\n",
              "      <td>5.000005</td>\n",
              "      <td>4.500004</td>\n",
              "      <td>3.499708</td>\n",
              "      <td>3.500003</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>2.500002</td>\n",
              "      <td>3.000003</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>1.000001</td>\n",
              "      <td>3.500003</td>\n",
              "      <td>1.500001</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>1.000001</td>\n",
              "      <td>1.500001</td>\n",
              "      <td>1.000001</td>\n",
              "      <td>4.500004</td>\n",
              "      <td>3.500003</td>\n",
              "      <td>3.000003</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>3.500003</td>\n",
              "      <td>4.000004</td>\n",
              "      <td>3.500003</td>\n",
              "      <td>4.000004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.881024</td>\n",
              "      <td>3.421886</td>\n",
              "      <td>3.427186</td>\n",
              "      <td>2.239842</td>\n",
              "      <td>3.057280</td>\n",
              "      <td>3.946683</td>\n",
              "      <td>3.197075</td>\n",
              "      <td>2.748549</td>\n",
              "      <td>2.921073</td>\n",
              "      <td>3.513596</td>\n",
              "      <td>3.709638</td>\n",
              "      <td>2.330166</td>\n",
              "      <td>3.129011</td>\n",
              "      <td>3.946833</td>\n",
              "      <td>2.992122</td>\n",
              "      <td>3.928751</td>\n",
              "      <td>3.817044</td>\n",
              "      <td>3.623509</td>\n",
              "      <td>2.749023</td>\n",
              "      <td>2.709416</td>\n",
              "      <td>2.999745</td>\n",
              "      <td>3.281466</td>\n",
              "      <td>3.146324</td>\n",
              "      <td>3.105717</td>\n",
              "      <td>3.659938</td>\n",
              "      <td>3.462677</td>\n",
              "      <td>3.478352</td>\n",
              "      <td>4.052634</td>\n",
              "      <td>3.926144</td>\n",
              "      <td>2.994820</td>\n",
              "      <td>3.182435</td>\n",
              "      <td>1.983056</td>\n",
              "      <td>3.736292</td>\n",
              "      <td>3.761480</td>\n",
              "      <td>2.001490</td>\n",
              "      <td>3.283147</td>\n",
              "      <td>3.499933</td>\n",
              "      <td>3.795573</td>\n",
              "      <td>3.170177</td>\n",
              "      <td>3.194634</td>\n",
              "      <td>...</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>3.499933</td>\n",
              "      <td>2.500395</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>3.251140</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>2.999942</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>1.999962</td>\n",
              "      <td>4.748841</td>\n",
              "      <td>2.999942</td>\n",
              "      <td>2.749860</td>\n",
              "      <td>3.256529</td>\n",
              "      <td>3.505547</td>\n",
              "      <td>4.145955</td>\n",
              "      <td>3.500337</td>\n",
              "      <td>4.999904</td>\n",
              "      <td>4.499913</td>\n",
              "      <td>3.502930</td>\n",
              "      <td>3.499933</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>2.499952</td>\n",
              "      <td>2.999942</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>0.999981</td>\n",
              "      <td>3.499933</td>\n",
              "      <td>1.499971</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>0.999981</td>\n",
              "      <td>1.499971</td>\n",
              "      <td>0.999981</td>\n",
              "      <td>4.499913</td>\n",
              "      <td>3.499933</td>\n",
              "      <td>2.999942</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>3.499933</td>\n",
              "      <td>3.999923</td>\n",
              "      <td>3.499933</td>\n",
              "      <td>3.999923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.018416</td>\n",
              "      <td>3.400912</td>\n",
              "      <td>3.493598</td>\n",
              "      <td>2.184629</td>\n",
              "      <td>3.156865</td>\n",
              "      <td>3.946429</td>\n",
              "      <td>3.246271</td>\n",
              "      <td>2.874286</td>\n",
              "      <td>2.903455</td>\n",
              "      <td>3.256205</td>\n",
              "      <td>3.837746</td>\n",
              "      <td>2.257888</td>\n",
              "      <td>3.091730</td>\n",
              "      <td>3.976458</td>\n",
              "      <td>2.982567</td>\n",
              "      <td>3.866928</td>\n",
              "      <td>3.457249</td>\n",
              "      <td>3.638830</td>\n",
              "      <td>2.668557</td>\n",
              "      <td>2.657656</td>\n",
              "      <td>3.884664</td>\n",
              "      <td>3.295209</td>\n",
              "      <td>3.172401</td>\n",
              "      <td>2.911738</td>\n",
              "      <td>3.736052</td>\n",
              "      <td>3.431177</td>\n",
              "      <td>3.461068</td>\n",
              "      <td>4.039688</td>\n",
              "      <td>3.996906</td>\n",
              "      <td>2.965153</td>\n",
              "      <td>3.146463</td>\n",
              "      <td>4.077958</td>\n",
              "      <td>3.686273</td>\n",
              "      <td>3.870821</td>\n",
              "      <td>2.001778</td>\n",
              "      <td>3.067054</td>\n",
              "      <td>3.500120</td>\n",
              "      <td>3.804698</td>\n",
              "      <td>3.175492</td>\n",
              "      <td>3.171672</td>\n",
              "      <td>...</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>3.500120</td>\n",
              "      <td>2.483630</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>3.249906</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>3.000103</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>2.000069</td>\n",
              "      <td>4.749042</td>\n",
              "      <td>3.000103</td>\n",
              "      <td>2.752446</td>\n",
              "      <td>3.241694</td>\n",
              "      <td>3.502344</td>\n",
              "      <td>4.092567</td>\n",
              "      <td>3.491996</td>\n",
              "      <td>5.000172</td>\n",
              "      <td>4.500155</td>\n",
              "      <td>3.508815</td>\n",
              "      <td>3.500120</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>2.500086</td>\n",
              "      <td>3.000103</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>1.000034</td>\n",
              "      <td>3.500120</td>\n",
              "      <td>1.500052</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>1.000034</td>\n",
              "      <td>1.500052</td>\n",
              "      <td>1.000034</td>\n",
              "      <td>4.500155</td>\n",
              "      <td>3.500120</td>\n",
              "      <td>3.000103</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>3.500120</td>\n",
              "      <td>4.000138</td>\n",
              "      <td>3.500120</td>\n",
              "      <td>4.000138</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 8938 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     1         2         3       ...    193581    193587    193609\n",
              "1  3.908820  3.390957  4.035030  ...  3.999934  3.499942  3.999934\n",
              "2  3.911434  3.445683  3.461056  ...  3.999768  3.499797  3.999768\n",
              "3  3.896327  3.383747  3.439498  ...  4.000004  3.500003  4.000004\n",
              "4  3.881024  3.421886  3.427186  ...  3.999923  3.499933  3.999923\n",
              "5  4.018416  3.400912  3.493598  ...  4.000138  3.500120  4.000138\n",
              "\n",
              "[5 rows x 8938 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUM1mXDfH0fs"
      },
      "source": [
        "item_factors, user_factors = get_svd(sparse_matrix_withuser)\r\n",
        "prediction_result_df = pd.DataFrame(np.matmul(item_factors, user_factors),\r\n",
        "                                    columns=sparse_matrix_withuser.columns.values, index=sparse_matrix_withuser.index.values)\r\n",
        "\r\n",
        "user_prediction_result_df = prediction_result_df.transpose()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "AXlS8oy7IUXo",
        "outputId": "193592a5-4160-41d2-b85c-b121f93eab96"
      },
      "source": [
        "user_prediction_result_df.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>34</th>\n",
              "      <th>36</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>...</th>\n",
              "      <th>184257</th>\n",
              "      <th>184349</th>\n",
              "      <th>184471</th>\n",
              "      <th>184721</th>\n",
              "      <th>184791</th>\n",
              "      <th>184931</th>\n",
              "      <th>184987</th>\n",
              "      <th>184997</th>\n",
              "      <th>185033</th>\n",
              "      <th>185135</th>\n",
              "      <th>185435</th>\n",
              "      <th>185585</th>\n",
              "      <th>187031</th>\n",
              "      <th>187541</th>\n",
              "      <th>187593</th>\n",
              "      <th>187595</th>\n",
              "      <th>187717</th>\n",
              "      <th>188189</th>\n",
              "      <th>188301</th>\n",
              "      <th>188675</th>\n",
              "      <th>188797</th>\n",
              "      <th>189043</th>\n",
              "      <th>189111</th>\n",
              "      <th>189333</th>\n",
              "      <th>189547</th>\n",
              "      <th>190183</th>\n",
              "      <th>190207</th>\n",
              "      <th>190209</th>\n",
              "      <th>190213</th>\n",
              "      <th>190215</th>\n",
              "      <th>190219</th>\n",
              "      <th>191005</th>\n",
              "      <th>193565</th>\n",
              "      <th>193567</th>\n",
              "      <th>193571</th>\n",
              "      <th>193573</th>\n",
              "      <th>193579</th>\n",
              "      <th>193581</th>\n",
              "      <th>193587</th>\n",
              "      <th>193609</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.362666</td>\n",
              "      <td>4.366280</td>\n",
              "      <td>3.966290</td>\n",
              "      <td>4.335373</td>\n",
              "      <td>4.363704</td>\n",
              "      <td>3.983660</td>\n",
              "      <td>4.303573</td>\n",
              "      <td>4.339269</td>\n",
              "      <td>4.305649</td>\n",
              "      <td>4.254211</td>\n",
              "      <td>4.282134</td>\n",
              "      <td>4.272191</td>\n",
              "      <td>4.305426</td>\n",
              "      <td>4.292480</td>\n",
              "      <td>4.367508</td>\n",
              "      <td>4.329744</td>\n",
              "      <td>4.236116</td>\n",
              "      <td>4.315804</td>\n",
              "      <td>4.335157</td>\n",
              "      <td>4.349928</td>\n",
              "      <td>4.390426</td>\n",
              "      <td>4.328058</td>\n",
              "      <td>4.333100</td>\n",
              "      <td>4.285464</td>\n",
              "      <td>4.297623</td>\n",
              "      <td>4.293682</td>\n",
              "      <td>4.293528</td>\n",
              "      <td>4.319689</td>\n",
              "      <td>4.356478</td>\n",
              "      <td>4.329777</td>\n",
              "      <td>4.321528</td>\n",
              "      <td>4.346353</td>\n",
              "      <td>4.321307</td>\n",
              "      <td>4.352456</td>\n",
              "      <td>4.322469</td>\n",
              "      <td>4.311651</td>\n",
              "      <td>4.336900</td>\n",
              "      <td>4.315834</td>\n",
              "      <td>4.319297</td>\n",
              "      <td>4.310632</td>\n",
              "      <td>...</td>\n",
              "      <td>4.320553</td>\n",
              "      <td>4.320659</td>\n",
              "      <td>4.319881</td>\n",
              "      <td>4.320247</td>\n",
              "      <td>4.320783</td>\n",
              "      <td>4.320052</td>\n",
              "      <td>4.320922</td>\n",
              "      <td>4.319682</td>\n",
              "      <td>4.321792</td>\n",
              "      <td>4.319183</td>\n",
              "      <td>4.320103</td>\n",
              "      <td>4.320393</td>\n",
              "      <td>4.321417</td>\n",
              "      <td>4.343645</td>\n",
              "      <td>4.316166</td>\n",
              "      <td>4.320900</td>\n",
              "      <td>4.319551</td>\n",
              "      <td>4.319899</td>\n",
              "      <td>4.319971</td>\n",
              "      <td>4.321276</td>\n",
              "      <td>4.320052</td>\n",
              "      <td>4.319997</td>\n",
              "      <td>4.320593</td>\n",
              "      <td>4.320508</td>\n",
              "      <td>4.338351</td>\n",
              "      <td>4.321614</td>\n",
              "      <td>4.318804</td>\n",
              "      <td>4.321786</td>\n",
              "      <td>4.318208</td>\n",
              "      <td>4.318804</td>\n",
              "      <td>4.318208</td>\n",
              "      <td>4.320182</td>\n",
              "      <td>4.320834</td>\n",
              "      <td>4.321160</td>\n",
              "      <td>4.320508</td>\n",
              "      <td>4.320508</td>\n",
              "      <td>4.320834</td>\n",
              "      <td>4.320508</td>\n",
              "      <td>4.320834</td>\n",
              "      <td>4.321664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.018066</td>\n",
              "      <td>3.941879</td>\n",
              "      <td>3.903070</td>\n",
              "      <td>3.960808</td>\n",
              "      <td>3.907779</td>\n",
              "      <td>3.993262</td>\n",
              "      <td>3.913604</td>\n",
              "      <td>3.941605</td>\n",
              "      <td>3.919774</td>\n",
              "      <td>3.880579</td>\n",
              "      <td>3.943441</td>\n",
              "      <td>3.918753</td>\n",
              "      <td>3.942259</td>\n",
              "      <td>3.949975</td>\n",
              "      <td>3.945655</td>\n",
              "      <td>3.897922</td>\n",
              "      <td>4.026012</td>\n",
              "      <td>3.962097</td>\n",
              "      <td>3.916821</td>\n",
              "      <td>3.927611</td>\n",
              "      <td>3.939632</td>\n",
              "      <td>3.939612</td>\n",
              "      <td>3.967440</td>\n",
              "      <td>3.959351</td>\n",
              "      <td>3.898365</td>\n",
              "      <td>3.929732</td>\n",
              "      <td>3.950717</td>\n",
              "      <td>3.966032</td>\n",
              "      <td>3.944258</td>\n",
              "      <td>3.943119</td>\n",
              "      <td>3.921734</td>\n",
              "      <td>3.899026</td>\n",
              "      <td>3.973978</td>\n",
              "      <td>3.966196</td>\n",
              "      <td>3.939077</td>\n",
              "      <td>3.943800</td>\n",
              "      <td>3.942287</td>\n",
              "      <td>3.925650</td>\n",
              "      <td>3.936770</td>\n",
              "      <td>3.931904</td>\n",
              "      <td>...</td>\n",
              "      <td>3.943699</td>\n",
              "      <td>3.940000</td>\n",
              "      <td>3.937679</td>\n",
              "      <td>3.938932</td>\n",
              "      <td>3.942038</td>\n",
              "      <td>3.940905</td>\n",
              "      <td>3.939593</td>\n",
              "      <td>3.947036</td>\n",
              "      <td>3.938280</td>\n",
              "      <td>3.913061</td>\n",
              "      <td>3.935446</td>\n",
              "      <td>3.946262</td>\n",
              "      <td>3.938680</td>\n",
              "      <td>3.914377</td>\n",
              "      <td>3.948401</td>\n",
              "      <td>3.940092</td>\n",
              "      <td>3.937104</td>\n",
              "      <td>3.938018</td>\n",
              "      <td>3.946582</td>\n",
              "      <td>3.942487</td>\n",
              "      <td>3.940905</td>\n",
              "      <td>3.935944</td>\n",
              "      <td>3.939630</td>\n",
              "      <td>3.941626</td>\n",
              "      <td>3.947114</td>\n",
              "      <td>3.938485</td>\n",
              "      <td>3.928572</td>\n",
              "      <td>3.947002</td>\n",
              "      <td>3.924886</td>\n",
              "      <td>3.928572</td>\n",
              "      <td>3.924886</td>\n",
              "      <td>3.945287</td>\n",
              "      <td>3.937965</td>\n",
              "      <td>3.934304</td>\n",
              "      <td>3.941626</td>\n",
              "      <td>3.941626</td>\n",
              "      <td>3.937965</td>\n",
              "      <td>3.941626</td>\n",
              "      <td>3.937965</td>\n",
              "      <td>3.940416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.503743</td>\n",
              "      <td>2.498854</td>\n",
              "      <td>2.491555</td>\n",
              "      <td>2.529266</td>\n",
              "      <td>2.509395</td>\n",
              "      <td>2.519116</td>\n",
              "      <td>2.486491</td>\n",
              "      <td>2.511599</td>\n",
              "      <td>2.508844</td>\n",
              "      <td>2.517707</td>\n",
              "      <td>2.511558</td>\n",
              "      <td>2.514186</td>\n",
              "      <td>2.516351</td>\n",
              "      <td>2.554171</td>\n",
              "      <td>2.534299</td>\n",
              "      <td>2.479551</td>\n",
              "      <td>2.544206</td>\n",
              "      <td>2.512989</td>\n",
              "      <td>2.491814</td>\n",
              "      <td>2.504471</td>\n",
              "      <td>2.503388</td>\n",
              "      <td>2.517612</td>\n",
              "      <td>2.511993</td>\n",
              "      <td>2.530817</td>\n",
              "      <td>2.516081</td>\n",
              "      <td>2.513982</td>\n",
              "      <td>2.516984</td>\n",
              "      <td>2.517356</td>\n",
              "      <td>2.495134</td>\n",
              "      <td>2.513022</td>\n",
              "      <td>0.383380</td>\n",
              "      <td>2.518260</td>\n",
              "      <td>2.492373</td>\n",
              "      <td>2.525631</td>\n",
              "      <td>2.518249</td>\n",
              "      <td>2.479970</td>\n",
              "      <td>2.513929</td>\n",
              "      <td>2.519828</td>\n",
              "      <td>2.517863</td>\n",
              "      <td>2.516064</td>\n",
              "      <td>...</td>\n",
              "      <td>2.516750</td>\n",
              "      <td>2.516683</td>\n",
              "      <td>2.515888</td>\n",
              "      <td>2.515541</td>\n",
              "      <td>2.516895</td>\n",
              "      <td>2.517157</td>\n",
              "      <td>2.516447</td>\n",
              "      <td>2.515947</td>\n",
              "      <td>2.515737</td>\n",
              "      <td>2.514842</td>\n",
              "      <td>2.517383</td>\n",
              "      <td>2.517969</td>\n",
              "      <td>2.516073</td>\n",
              "      <td>2.522945</td>\n",
              "      <td>2.515949</td>\n",
              "      <td>2.516545</td>\n",
              "      <td>2.513610</td>\n",
              "      <td>2.514575</td>\n",
              "      <td>2.516228</td>\n",
              "      <td>2.516513</td>\n",
              "      <td>2.517157</td>\n",
              "      <td>2.519283</td>\n",
              "      <td>2.516905</td>\n",
              "      <td>2.516569</td>\n",
              "      <td>2.524595</td>\n",
              "      <td>2.517604</td>\n",
              "      <td>2.524039</td>\n",
              "      <td>2.512149</td>\n",
              "      <td>2.526417</td>\n",
              "      <td>2.524039</td>\n",
              "      <td>2.526417</td>\n",
              "      <td>2.516350</td>\n",
              "      <td>2.516789</td>\n",
              "      <td>2.517009</td>\n",
              "      <td>2.516569</td>\n",
              "      <td>2.516569</td>\n",
              "      <td>2.516789</td>\n",
              "      <td>2.516569</td>\n",
              "      <td>2.516789</td>\n",
              "      <td>2.516032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.624253</td>\n",
              "      <td>3.635961</td>\n",
              "      <td>3.619964</td>\n",
              "      <td>3.641844</td>\n",
              "      <td>3.632798</td>\n",
              "      <td>3.643807</td>\n",
              "      <td>3.636671</td>\n",
              "      <td>3.642801</td>\n",
              "      <td>3.615370</td>\n",
              "      <td>3.632327</td>\n",
              "      <td>3.607290</td>\n",
              "      <td>3.649405</td>\n",
              "      <td>3.631232</td>\n",
              "      <td>3.640977</td>\n",
              "      <td>3.642348</td>\n",
              "      <td>3.633410</td>\n",
              "      <td>3.643807</td>\n",
              "      <td>3.632953</td>\n",
              "      <td>3.656087</td>\n",
              "      <td>3.642907</td>\n",
              "      <td>3.010115</td>\n",
              "      <td>3.641869</td>\n",
              "      <td>3.637811</td>\n",
              "      <td>3.641900</td>\n",
              "      <td>3.600203</td>\n",
              "      <td>3.624221</td>\n",
              "      <td>3.629186</td>\n",
              "      <td>3.639269</td>\n",
              "      <td>3.621811</td>\n",
              "      <td>3.632938</td>\n",
              "      <td>3.642877</td>\n",
              "      <td>1.975800</td>\n",
              "      <td>3.649815</td>\n",
              "      <td>3.633827</td>\n",
              "      <td>3.632849</td>\n",
              "      <td>3.597837</td>\n",
              "      <td>3.631389</td>\n",
              "      <td>3.620023</td>\n",
              "      <td>3.636043</td>\n",
              "      <td>3.621379</td>\n",
              "      <td>...</td>\n",
              "      <td>3.632061</td>\n",
              "      <td>3.631896</td>\n",
              "      <td>3.634660</td>\n",
              "      <td>3.631660</td>\n",
              "      <td>3.632287</td>\n",
              "      <td>3.631393</td>\n",
              "      <td>3.632082</td>\n",
              "      <td>3.630840</td>\n",
              "      <td>3.632771</td>\n",
              "      <td>3.632925</td>\n",
              "      <td>3.632303</td>\n",
              "      <td>3.632957</td>\n",
              "      <td>3.632240</td>\n",
              "      <td>3.641846</td>\n",
              "      <td>3.632617</td>\n",
              "      <td>3.632179</td>\n",
              "      <td>3.631302</td>\n",
              "      <td>3.631481</td>\n",
              "      <td>3.630569</td>\n",
              "      <td>3.631749</td>\n",
              "      <td>3.631393</td>\n",
              "      <td>3.631816</td>\n",
              "      <td>3.631864</td>\n",
              "      <td>3.632119</td>\n",
              "      <td>3.642411</td>\n",
              "      <td>3.630964</td>\n",
              "      <td>3.631722</td>\n",
              "      <td>3.631958</td>\n",
              "      <td>3.631674</td>\n",
              "      <td>3.631722</td>\n",
              "      <td>3.631674</td>\n",
              "      <td>3.632685</td>\n",
              "      <td>3.631554</td>\n",
              "      <td>3.630989</td>\n",
              "      <td>3.632119</td>\n",
              "      <td>3.632119</td>\n",
              "      <td>3.631554</td>\n",
              "      <td>3.632119</td>\n",
              "      <td>3.631554</td>\n",
              "      <td>3.632004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.051183</td>\n",
              "      <td>3.581114</td>\n",
              "      <td>3.721059</td>\n",
              "      <td>3.627674</td>\n",
              "      <td>3.519383</td>\n",
              "      <td>3.536805</td>\n",
              "      <td>3.768194</td>\n",
              "      <td>3.682646</td>\n",
              "      <td>3.571370</td>\n",
              "      <td>3.639438</td>\n",
              "      <td>3.758502</td>\n",
              "      <td>3.716790</td>\n",
              "      <td>3.593090</td>\n",
              "      <td>3.739135</td>\n",
              "      <td>3.544159</td>\n",
              "      <td>3.638336</td>\n",
              "      <td>3.573450</td>\n",
              "      <td>3.685931</td>\n",
              "      <td>3.703388</td>\n",
              "      <td>3.602880</td>\n",
              "      <td>3.767780</td>\n",
              "      <td>3.647955</td>\n",
              "      <td>3.633116</td>\n",
              "      <td>3.579576</td>\n",
              "      <td>3.781266</td>\n",
              "      <td>3.683309</td>\n",
              "      <td>3.644241</td>\n",
              "      <td>3.618433</td>\n",
              "      <td>3.674949</td>\n",
              "      <td>3.609795</td>\n",
              "      <td>3.644883</td>\n",
              "      <td>3.645444</td>\n",
              "      <td>3.694475</td>\n",
              "      <td>3.824025</td>\n",
              "      <td>3.630491</td>\n",
              "      <td>2.994323</td>\n",
              "      <td>3.637913</td>\n",
              "      <td>3.638693</td>\n",
              "      <td>3.650969</td>\n",
              "      <td>3.604851</td>\n",
              "      <td>...</td>\n",
              "      <td>3.634138</td>\n",
              "      <td>3.636475</td>\n",
              "      <td>3.619286</td>\n",
              "      <td>3.641151</td>\n",
              "      <td>3.637461</td>\n",
              "      <td>3.638865</td>\n",
              "      <td>3.635243</td>\n",
              "      <td>3.639514</td>\n",
              "      <td>3.631621</td>\n",
              "      <td>3.629838</td>\n",
              "      <td>3.637322</td>\n",
              "      <td>3.631094</td>\n",
              "      <td>3.633085</td>\n",
              "      <td>3.625826</td>\n",
              "      <td>3.694962</td>\n",
              "      <td>3.635317</td>\n",
              "      <td>3.649354</td>\n",
              "      <td>3.645253</td>\n",
              "      <td>3.637108</td>\n",
              "      <td>3.637206</td>\n",
              "      <td>3.638865</td>\n",
              "      <td>3.622887</td>\n",
              "      <td>3.635139</td>\n",
              "      <td>3.636919</td>\n",
              "      <td>3.664728</td>\n",
              "      <td>3.628336</td>\n",
              "      <td>3.598384</td>\n",
              "      <td>3.659643</td>\n",
              "      <td>3.586132</td>\n",
              "      <td>3.598384</td>\n",
              "      <td>3.586132</td>\n",
              "      <td>3.638168</td>\n",
              "      <td>3.635671</td>\n",
              "      <td>3.634422</td>\n",
              "      <td>3.636919</td>\n",
              "      <td>3.636919</td>\n",
              "      <td>3.635671</td>\n",
              "      <td>3.636919</td>\n",
              "      <td>3.635671</td>\n",
              "      <td>3.637667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 8938 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     1         2         3       ...    193581    193587    193609\n",
              "1  4.362666  4.366280  3.966290  ...  4.320508  4.320834  4.321664\n",
              "2  4.018066  3.941879  3.903070  ...  3.941626  3.937965  3.940416\n",
              "3  2.503743  2.498854  2.491555  ...  2.516569  2.516789  2.516032\n",
              "4  3.624253  3.635961  3.619964  ...  3.632119  3.631554  3.632004\n",
              "5  4.051183  3.581114  3.721059  ...  3.636919  3.635671  3.637667\n",
              "\n",
              "[5 rows x 8938 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpX4VZk0IYew"
      },
      "source": [
        "# 성능 비교\r\n",
        "\r\n",
        "def evaluate(test_df, prediction_result_df):\r\n",
        "  groups_with_movie_ids = test_df.groupby(by='movieId')\r\n",
        "  groups_with_user_ids = test_df.groupby(by='userId')\r\n",
        "  intersection_movie_ids = sorted(list(set(list(prediction_result_df.columns)).intersection(set(list(groups_with_movie_ids.indices.keys())))))\r\n",
        "  intersection_user_ids = sorted(list(set(list(prediction_result_df.index)).intersection(set(groups_with_user_ids.indices.keys()))))\r\n",
        "\r\n",
        "  print(len(intersection_movie_ids))\r\n",
        "  print(len(intersection_user_ids))\r\n",
        "\r\n",
        "  compressed_prediction_df = prediction_result_df.loc[intersection_user_ids][intersection_movie_ids]\r\n",
        "\r\n",
        "  # test_df에 대해서 RMSE 계산\r\n",
        "  grouped = test_df.groupby(by='userId')\r\n",
        "  rmse_df = pd.DataFrame(columns=['rmse'])\r\n",
        "  for userId, group in tqdm(grouped):\r\n",
        "      if userId in intersection_user_ids:\r\n",
        "          pred_ratings = compressed_prediction_df.loc[userId][compressed_prediction_df.loc[userId].index.intersection(list(group['movieId'].values))]\r\n",
        "          pred_ratings = pred_ratings.to_frame(name='rating').reset_index().rename(columns={'index':'movieId','rating':'pred_rating'})\r\n",
        "          actual_ratings = group[['rating', 'movieId']].rename(columns={'rating':'actual_rating'})\r\n",
        "\r\n",
        "          final_df = pd.merge(actual_ratings, pred_ratings, how='inner', on=['movieId'])\r\n",
        "          final_df = final_df.round(4) # 반올림\r\n",
        "          \r\n",
        "          if not final_df.empty:\r\n",
        "            rmse = sqrt(mean_squared_error(final_df['actual_rating'], final_df['pred_rating']))\r\n",
        "            rmse_df.loc[userId] = rmse\r\n",
        "\r\n",
        "  return final_df, rmse_df"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418,
          "referenced_widgets": [
            "0d7ddc603ed34fadbb3b7fb16286ba1c",
            "41d14a4cf97c4e1c8de54351a2d633ca",
            "f4faf2cbde194b14bdbcb1e8579bdbb8",
            "7512d9020c9a42e09fe3f9828885ce4f",
            "b75bf30bb21743889f7570ee7dcc11e8",
            "017d1140efcc4e209b720994383df088",
            "478d6c291c4c4e05808ef021c6648823",
            "a1d90bd78e904ef89ad4e7f79af26f32"
          ]
        },
        "id": "OJQHDj5_I4tA",
        "outputId": "0f18fccd-d4f4-464c-8866-c97559a4095e"
      },
      "source": [
        "result_df, _ = evaluate(test_df, user_prediction_result_df)\r\n",
        "print(result_df)\r\n",
        "print(\"For user matrix\")\r\n",
        "print(f\"RMSE: {sqrt(mean_squared_error(result_df['actual_rating'].values, result_df['pred_rating'].values))}\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4385\n",
            "610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0d7ddc603ed34fadbb3b7fb16286ba1c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "     actual_rating  movieId  pred_rating\n",
            "0              5.0     3527       3.6789\n",
            "1              3.5    84772       3.6783\n",
            "2              3.5   103141       3.6758\n",
            "3              4.0    81132       3.6785\n",
            "4              4.5   130634       3.6785\n",
            "..             ...      ...          ...\n",
            "218            4.0   106100       3.6805\n",
            "219            4.0   111759       3.6747\n",
            "220            1.0     4852       3.6787\n",
            "221            3.0     2628       3.6883\n",
            "222            5.0     1953       3.6721\n",
            "\n",
            "[223 rows x 3 columns]\n",
            "For user matrix\n",
            "RMSE: 0.8551691150767583\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvKJokWII54W"
      },
      "source": [
        "# 0.85의 rmse값"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418,
          "referenced_widgets": [
            "929c243a37f042529b7824255134eb0e",
            "656594997e2f4567a4eaf1a9bc08276c",
            "57310a2483f940549e1a89bb95b46431",
            "2635606c70614e7295cf417c59138fb2",
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            "cefcab69b20141b6b691651cb387d2dc",
            "ea413ead824b4ee1a87b7d92e75943d2",
            "0159820f9cb34c52a8cc784af981b6e5"
          ]
        },
        "id": "cc2QvfVlJIHO",
        "outputId": "f5cfa82f-00bb-4e6d-dd75-decf427b3b23"
      },
      "source": [
        "result_df, _ = evaluate(test_df, movie_prediction_result_df)\r\n",
        "print(result_df)\r\n",
        "print(\"For movie matrix\")\r\n",
        "print(f\"RMSE: {sqrt(mean_squared_error(result_df['actual_rating'].values, result_df['pred_rating'].values))}\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4385\n",
            "610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "929c243a37f042529b7824255134eb0e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "     actual_rating  movieId  pred_rating\n",
            "0              5.0     3527       3.6030\n",
            "1              3.5    84772       3.0433\n",
            "2              3.5   103141       4.0453\n",
            "3              4.0    81132       3.5000\n",
            "4              4.5   130634       3.2496\n",
            "..             ...      ...          ...\n",
            "218            4.0   106100       3.8314\n",
            "219            4.0   111759       3.9239\n",
            "220            1.0     4852       1.0000\n",
            "221            3.0     2628       3.1901\n",
            "222            5.0     1953       3.8234\n",
            "\n",
            "[223 rows x 3 columns]\n",
            "For movie matrix\n",
            "RMSE: 0.8209361471321392\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "772O0TsLJK8y"
      },
      "source": [
        "# rmse값이 조금 더 감소함"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKDBoYLMJVsG"
      },
      "source": [
        "# 그렇다면 최적의 K는 과연 무엇일까?"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ev97wl_aJZRf"
      },
      "source": [
        "def find_best_k(sparse_matrix, maximum_k=100):\r\n",
        "    print(\"\\nFind best optimized k for Matrix Factorization\")\r\n",
        "    k_candidates = np.arange(50, maximum_k, 10)\r\n",
        "    final_df = pd.DataFrame(columns=['rmse'], index=k_candidates)\r\n",
        "    for k in tqdm(k_candidates):\r\n",
        "        item_factors, user_factors = get_svd(sparse_matrix, k)\r\n",
        "        each_results_df = pd.DataFrame(np.matmul(item_factors, user_factors),\r\n",
        "                                    columns=sparse_matrix.columns.values, index=sparse_matrix.index.values)\r\n",
        "        each_results_df = each_results_df.transpose()\r\n",
        "        \r\n",
        "        result_df, _ = evaluate(test_df, each_results_df)\r\n",
        "        each_rmse = sqrt(mean_squared_error(result_df['actual_rating'].values, result_df['pred_rating'].values))\r\n",
        "\r\n",
        "        final_df.loc[k]['rmse'] = each_rmse\r\n",
        "\r\n",
        "    return final_df\r\n"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SP9DbLm-Jlmm"
      },
      "source": [
        "# 무비를 가지고 돌려보자 왜냐하면 무비가 점수가 좀 더 좋았었기 때문이다"
      ],
      "execution_count": 39,
      "outputs": []
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        "id": "-XfbIskyJqyZ",
        "outputId": "ac9756f2-8c08-4400-fb5b-984083e84600"
      },
      "source": [
        "res = find_best_k(sparse_matrix_withmovie,200)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Find best optimized k for Matrix Factorization\n"
          ],
          "name": "stdout"
        },
        {
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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            "4385\n",
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
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        {
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            "4385\n",
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
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            "4385\n",
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
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            "4385\n",
            "610\n"
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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          "text": [
            "\n",
            "4385\n",
            "610\n"
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "model_id": "97840628cf3146bd88a11f55fab882c2",
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              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "4385\n",
            "610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
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          "metadata": {
            "tags": []
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "4385\n",
            "610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "4385\n",
            "610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
            ]
          },
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            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "4385\n",
            "610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "4385\n",
            "610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "4385\n",
            "610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1241e294003e4e73b8b3e48d2a2d76b1",
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              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=610.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJz5q7VJReUn"
      },
      "source": [
        "# 그리드 서치 방법임"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "HWc48XoRRgYv",
        "outputId": "2899c1bf-b799-4ad0-86b7-ba8c0ab3fec2"
      },
      "source": [
        "res"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rmse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.823432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.826151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>0.825998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>0.821733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>0.823385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>0.824104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>0.823003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>0.82306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>0.822952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>0.821839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>0.821476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>0.822325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>0.822644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>0.822342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>0.822116</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         rmse\n",
              "50   0.823432\n",
              "60   0.826151\n",
              "70   0.825998\n",
              "80   0.821733\n",
              "90   0.823385\n",
              "100  0.824104\n",
              "110  0.823003\n",
              "120   0.82306\n",
              "130  0.822952\n",
              "140  0.821839\n",
              "150  0.821476\n",
              "160  0.822325\n",
              "170  0.822644\n",
              "180  0.822342\n",
              "190  0.822116"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "id": "RqT2a-PoJ0V1",
        "outputId": "37d94d98-8659-4c6b-a142-9dfe167ce6f6"
      },
      "source": [
        "res[res.rmse==res.rmse.min()]"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rmse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>0.821476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         rmse\n",
              "150  0.821476"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "f8Ymnx4eQobT",
        "outputId": "ecfe8112-620b-4c55-faf5-db24f263a0a5"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "plt.plot(res.index, res.rmse)\r\n",
        "plt.title(\"Find best optimized k for Matrix Factorization\", fontsize=20)\r\n",
        "plt.xlabel('number of k', fontsize=15)\r\n",
        "plt.ylabel('rmse', fontsize=15)\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# k가 작으면 압축시키기 힘들다 는 것을 보여주는거같음\r\n",
        "# k: 하이퍼파라미터"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUPOW_IQRmo5"
      },
      "source": [
        "# Matrix Factorization with simple python module"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "Ghp90-WzSORq",
        "outputId": "a6a7b91e-d7c1-46c3-f338-d945cf0a1441"
      },
      "source": [
        "!pip install matrix-factorization"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting matrix-factorization\n",
            "  Downloading https://files.pythonhosted.org/packages/89/12/57f3ef3485604d12353d1bb921387d2d9063f78f9d96d9abe06312c58f98/matrix_factorization-1.3.tar.gz\n",
            "Requirement already satisfied: numba>=0.49.1 in /usr/local/lib/python3.7/dist-packages (from matrix-factorization) (0.51.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.7/dist-packages (from matrix-factorization) (1.19.5)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from matrix-factorization) (1.1.5)\n",
            "Collecting scikit-learn>=0.23.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f3/74/eb899f41d55f957e2591cde5528e75871f817d9fb46d4732423ecaca736d/scikit_learn-0.24.1-cp37-cp37m-manylinux2010_x86_64.whl (22.3MB)\n",
            "\u001b[K     |████████████████████████████████| 22.3MB 1.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from matrix-factorization) (1.4.1)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.49.1->matrix-factorization) (0.34.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49.1->matrix-factorization) (54.0.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.4->matrix-factorization) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.4->matrix-factorization) (2018.9)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.23.1->matrix-factorization) (1.0.1)\n",
            "Collecting threadpoolctl>=2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0.4->matrix-factorization) (1.15.0)\n",
            "Building wheels for collected packages: matrix-factorization\n",
            "  Building wheel for matrix-factorization (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for matrix-factorization: filename=matrix_factorization-1.3-cp37-none-any.whl size=16468 sha256=43c0ec49002ca8b34f88a903c54eadbcf6fb3ff8bce049d3c30a0ad170d6c833\n",
            "  Stored in directory: /root/.cache/pip/wheels/db/ef/99/19df32b6eb88678b0e3a42d2e24955e7753b2de44615111bad\n",
            "Successfully built matrix-factorization\n",
            "Installing collected packages: threadpoolctl, scikit-learn, matrix-factorization\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "Successfully installed matrix-factorization-1.3 scikit-learn-0.24.1 threadpoolctl-2.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "sklearn"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fA5A_WkSaUY"
      },
      "source": [
        "from matrix_factorization import BaselineModel, KernelMF, train_update_test_split"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "iyRliT1_TU1P",
        "outputId": "018a2781-30d3-448f-a08d-8732f6366a77"
      },
      "source": [
        "ratings_df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
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          },
          "metadata": {
            "tags": []
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          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfnEe4v0TWxq"
      },
      "source": [
        "train_df, test_df = train_test_split(ratings_df, test_size = 0.2, random_state=1234)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
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        "id": "3sqyS3-TTnEw",
        "outputId": "ef98501a-5af7-40c9-f188-263527654081"
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      "source": [
        "train_df.head()"
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      "execution_count": 10,
      "outputs": [
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            ],
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              "       userId  movieId  rating   timestamp\n",
              "95713     600     5943     3.0  1237714356\n",
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          },
          "metadata": {
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KY8fV0FCToiX",
        "outputId": "9a298439-4f03-43a3-ec92-d0bf7df2d261"
      },
      "source": [
        "train_df.columns"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6uY5MzjkTpRq"
      },
      "source": [
        "new_train_df = train_df"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "KUIGO39GX9Xt",
        "outputId": "bca4badf-73a0-4f2f-a08f-1fc10aa94eb6"
      },
      "source": [
        "new_train_df = new_train_df.rename(columns = {'userId' : 'user_id', 'movieId' : 'item_id'})\r\n",
        "new_train_df.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    </tr>\n",
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              "</div>"
            ],
            "text/plain": [
              "       user_id  item_id  rating   timestamp\n",
              "95713      600     5943     3.0  1237714356\n",
              "61560      407     2571     5.0  1424349171\n",
              "77204      482     8958     4.0  1105397126\n",
              "93367      599     2322     2.5  1498515283\n",
              "90892      590     2959     3.5  1258416553"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERZY1HvdYApc"
      },
      "source": [
        "# train과 validation set 을 활용하는 함수임"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfSdotGKZQRo"
      },
      "source": [
        "(\r\n",
        "    X_train_initial,\r\n",
        "    y_train_initial,\r\n",
        "    X_train_update,\r\n",
        "    y_train_update,\r\n",
        "    X_test_update,\r\n",
        "    y_test_update,\r\n",
        " \r\n",
        ") = train_update_test_split(new_train_df, frac_new_users=0.2)\r\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQWrUnG2aHDO",
        "outputId": "3b1a1f90-2de8-4cf6-c860-d3df002e3c94"
      },
      "source": [
        "# Initial Training\r\n",
        "matrix_fact = KernelMF(n_epochs=20, n_factors=100, verbose=1, lr=0.001, reg=0.005) # 데이터를 20번 에폭시킨다, \r\n",
        "matrix_fact.fit(X_train_initial,y_train_initial)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch  1 / 20  -  train_rmse: 0.9902923734116843\n",
            "Epoch  2 / 20  -  train_rmse: 0.9634909376675221\n",
            "Epoch  3 / 20  -  train_rmse: 0.9459094418184527\n",
            "Epoch  4 / 20  -  train_rmse: 0.9328189417323592\n",
            "Epoch  5 / 20  -  train_rmse: 0.9224020732059239\n",
            "Epoch  6 / 20  -  train_rmse: 0.9136911333606168\n",
            "Epoch  7 / 20  -  train_rmse: 0.9061708898115689\n",
            "Epoch  8 / 20  -  train_rmse: 0.8995269854710846\n",
            "Epoch  9 / 20  -  train_rmse: 0.8935432931667722\n",
            "Epoch  10 / 20  -  train_rmse: 0.8880776646630345\n",
            "Epoch  11 / 20  -  train_rmse: 0.8830222660481146\n",
            "Epoch  12 / 20  -  train_rmse: 0.87829795350489\n",
            "Epoch  13 / 20  -  train_rmse: 0.873855267639862\n",
            "Epoch  14 / 20  -  train_rmse: 0.8696405982449501\n",
            "Epoch  15 / 20  -  train_rmse: 0.8656246465819045\n",
            "Epoch  16 / 20  -  train_rmse: 0.8617669068415242\n",
            "Epoch  17 / 20  -  train_rmse: 0.8580517049644107\n",
            "Epoch  18 / 20  -  train_rmse: 0.8544651471438953\n",
            "Epoch  19 / 20  -  train_rmse: 0.850982222035057\n",
            "Epoch  20 / 20  -  train_rmse: 0.8475912047148881\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KernelMF(gamma=0.01, lr=0.001, n_epochs=20, reg=0.005)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9r5P4qnbaJrc",
        "outputId": "72168c03-8d07-43a3-86ab-93ffbe47f5be"
      },
      "source": [
        "# update model with new users \r\n",
        "matrix_fact.update_users(\r\n",
        "    X_train_update, y_train_update, lr=0.001, n_epochs = 20, verbose =1\r\n",
        ")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch  1 / 20  -  train_rmse: 0.9513899678760789\n",
            "Epoch  2 / 20  -  train_rmse: 0.9337154041285947\n",
            "Epoch  3 / 20  -  train_rmse: 0.9251225041329685\n",
            "Epoch  4 / 20  -  train_rmse: 0.9193608952757902\n",
            "Epoch  5 / 20  -  train_rmse: 0.9147869052540241\n",
            "Epoch  6 / 20  -  train_rmse: 0.9108798841806494\n",
            "Epoch  7 / 20  -  train_rmse: 0.9074740909776742\n",
            "Epoch  8 / 20  -  train_rmse: 0.9044480027567015\n",
            "Epoch  9 / 20  -  train_rmse: 0.9017139777253673\n",
            "Epoch  10 / 20  -  train_rmse: 0.8992331146615838\n",
            "Epoch  11 / 20  -  train_rmse: 0.8969459017765313\n",
            "Epoch  12 / 20  -  train_rmse: 0.8948380205344544\n",
            "Epoch  13 / 20  -  train_rmse: 0.8928776878000508\n",
            "Epoch  14 / 20  -  train_rmse: 0.8910447064023277\n",
            "Epoch  15 / 20  -  train_rmse: 0.8893095636748353\n",
            "Epoch  16 / 20  -  train_rmse: 0.887663581684065\n",
            "Epoch  17 / 20  -  train_rmse: 0.886094534894821\n",
            "Epoch  18 / 20  -  train_rmse: 0.8845976867164034\n",
            "Epoch  19 / 20  -  train_rmse: 0.8831642235547614\n",
            "Epoch  20 / 20  -  train_rmse: 0.8817879362256213\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jhpCAQTYdMob",
        "outputId": "2ebd7c82-5d6f-4aba-ee63-b5bb2e1d0245"
      },
      "source": [
        "pred = matrix_fact.predict(X_test_update)\r\n",
        "rmse = mean_squared_error(y_test_update, pred, squared=False)\r\n",
        "print(f'\\nTest RMSE: {rmse:.4f}')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test RMSE: 0.9144\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "IFfI3aeMdvAW",
        "outputId": "19a3e516-40ae-413f-92fe-45e05f0d355f"
      },
      "source": [
        "# get recommendation\r\n",
        "\r\n",
        "user  = 200\r\n",
        "items_known = X_train_initial.query('user_id == @user')['item_id']\r\n",
        "matrix_fact.recommend(user=user, items_known=items_known)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>item_id</th>\n",
              "      <th>rating_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>462</th>\n",
              "      <td>200</td>\n",
              "      <td>858</td>\n",
              "      <td>4.452906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>200</td>\n",
              "      <td>50</td>\n",
              "      <td>4.446990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>200</td>\n",
              "      <td>260</td>\n",
              "      <td>4.444136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>200</td>\n",
              "      <td>1221</td>\n",
              "      <td>4.390458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>200</td>\n",
              "      <td>5618</td>\n",
              "      <td>4.385997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>200</td>\n",
              "      <td>527</td>\n",
              "      <td>4.385979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>200</td>\n",
              "      <td>110</td>\n",
              "      <td>4.359588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>852</th>\n",
              "      <td>200</td>\n",
              "      <td>2329</td>\n",
              "      <td>4.357202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604</th>\n",
              "      <td>200</td>\n",
              "      <td>912</td>\n",
              "      <td>4.345594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>200</td>\n",
              "      <td>2028</td>\n",
              "      <td>4.342974</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      user_id  item_id  rating_pred\n",
              "462       200      858     4.452906\n",
              "224       200       50     4.446990\n",
              "503       200      260     4.444136\n",
              "222       200     1221     4.390458\n",
              "111       200     5618     4.385997\n",
              "726       200      527     4.385979\n",
              "426       200      110     4.359588\n",
              "852       200     2329     4.357202\n",
              "604       200      912     4.345594\n",
              "1021      200     2028     4.342974"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNVUNM6VeMDw",
        "outputId": "5f6978a2-fa74-4b2c-923d-fb176215c6c5"
      },
      "source": [
        " # SGD\r\n",
        " \r\n",
        " baseline_model = BaselineModel(method='sgd', n_epochs=20, reg = 0.005, lr=0.01, verbose = 1)\r\n",
        " baseline_model.fit(X_train_initial, y_train_initial)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch  1 / 20  -  train_rmse: 0.9014570575437195\n",
            "Epoch  2 / 20  -  train_rmse: 0.8767951394034952\n",
            "Epoch  3 / 20  -  train_rmse: 0.8635079374764848\n",
            "Epoch  4 / 20  -  train_rmse: 0.8541385668217082\n",
            "Epoch  5 / 20  -  train_rmse: 0.8470387037023569\n",
            "Epoch  6 / 20  -  train_rmse: 0.841539098381603\n",
            "Epoch  7 / 20  -  train_rmse: 0.8372672703145612\n",
            "Epoch  8 / 20  -  train_rmse: 0.8327682460934016\n",
            "Epoch  9 / 20  -  train_rmse: 0.8294408896424242\n",
            "Epoch  10 / 20  -  train_rmse: 0.8265529660951629\n",
            "Epoch  11 / 20  -  train_rmse: 0.8236907674826788\n",
            "Epoch  12 / 20  -  train_rmse: 0.8211487317746308\n",
            "Epoch  13 / 20  -  train_rmse: 0.8189060386329027\n",
            "Epoch  14 / 20  -  train_rmse: 0.8171744659753521\n",
            "Epoch  15 / 20  -  train_rmse: 0.8150123206968948\n",
            "Epoch  16 / 20  -  train_rmse: 0.8135520773180508\n",
            "Epoch  17 / 20  -  train_rmse: 0.8121470813359517\n",
            "Epoch  18 / 20  -  train_rmse: 0.8107771799799226\n",
            "Epoch  19 / 20  -  train_rmse: 0.809581585841735\n",
            "Epoch  20 / 20  -  train_rmse: 0.8079893593695578\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaselineModel(n_epochs=20, reg=0.005)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4rrOsgZe1S7",
        "outputId": "2334cd0e-1a9d-420d-9bc0-080f9e1c806f"
      },
      "source": [
        "pred = baseline_model.predict(X_test_update)\r\n",
        "rmse = mean_squared_error(y_test_update, pred, squared=False)\r\n",
        "print(f'\\nTest RMSE: {rmse:.4f}') \r\n",
        "# test rmse는 좀 올라갔음. \r\n",
        "# 속도가 빠른 것 같음"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test RMSE: 0.9821\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wEGQCFlfAAm",
        "outputId": "4775ed85-263f-4a5d-af72-c417d0d4812c"
      },
      "source": [
        "# update \r\n",
        "\r\n",
        "%%time\r\n",
        "baseline_model.update_users(X_train_update, y_train_update, n_epochs=20, lr=0.001, verbose=1)\r\n",
        "pred = baseline_model.predict(X_test_update)\r\n",
        "rmse = mean_squared_error(y_test_update, pred, squared = False)\r\n",
        "\r\n",
        "print(f'\\nTest RMSE: {rmse:.4f}')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch  1 / 20  -  train_rmse: 0.9157209423249532\n",
            "Epoch  2 / 20  -  train_rmse: 0.8988421170896284\n",
            "Epoch  3 / 20  -  train_rmse: 0.8914014259707924\n",
            "Epoch  4 / 20  -  train_rmse: 0.8867650056624158\n",
            "Epoch  5 / 20  -  train_rmse: 0.8833570033894697\n",
            "Epoch  6 / 20  -  train_rmse: 0.8806170335680775\n",
            "Epoch  7 / 20  -  train_rmse: 0.878336380305179\n",
            "Epoch  8 / 20  -  train_rmse: 0.8763768716709025\n",
            "Epoch  9 / 20  -  train_rmse: 0.874697367497508\n",
            "Epoch  10 / 20  -  train_rmse: 0.87321010293861\n",
            "Epoch  11 / 20  -  train_rmse: 0.8719099837516985\n",
            "Epoch  12 / 20  -  train_rmse: 0.8707510787216719\n",
            "Epoch  13 / 20  -  train_rmse: 0.8697208622453109\n",
            "Epoch  14 / 20  -  train_rmse: 0.8687855061869498\n",
            "Epoch  15 / 20  -  train_rmse: 0.8679388162075797\n",
            "Epoch  16 / 20  -  train_rmse: 0.8671624160276711\n",
            "Epoch  17 / 20  -  train_rmse: 0.8664572699511555\n",
            "Epoch  18 / 20  -  train_rmse: 0.8658088598744527\n",
            "Epoch  19 / 20  -  train_rmse: 0.8652095978112708\n",
            "Epoch  20 / 20  -  train_rmse: 0.8646565196474231\n",
            "\n",
            "Test RMSE: 0.8827\n",
            "CPU times: user 734 ms, sys: 9.27 ms, total: 744 ms\n",
            "Wall time: 744 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIZJZ5D2fOk5"
      },
      "source": [
        "# ALS"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBb9bQV8fU7E",
        "outputId": "22502886-0373-4ad8-e10b-55d4ce81fdcd"
      },
      "source": [
        "baseline_model = BaselineModel(method='als', n_epochs = 20, reg = 0.5, verbose=1)\r\n",
        "baseline_model.fit(X_train_initial, y_train_initial)\r\n",
        "\r\n",
        "pred = baseline_model.predict(X_test_update)\r\n",
        "rmse = mean_squared_error(y_test_update, pred, squared = False)\r\n",
        "\r\n",
        "print(f'\\nTest RMSE: {rmse:.4f}')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch  1 / 20  -  train_rmse: 0.7963492330841759\n",
            "Epoch  2 / 20  -  train_rmse: 0.7808869656537913\n",
            "Epoch  3 / 20  -  train_rmse: 0.7795133041780553\n",
            "Epoch  4 / 20  -  train_rmse: 0.7792954056618131\n",
            "Epoch  5 / 20  -  train_rmse: 0.7792411031636738\n",
            "Epoch  6 / 20  -  train_rmse: 0.7792211266535206\n",
            "Epoch  7 / 20  -  train_rmse: 0.7792103589474602\n",
            "Epoch  8 / 20  -  train_rmse: 0.7792026848751586\n",
            "Epoch  9 / 20  -  train_rmse: 0.7791963888909268\n",
            "Epoch  10 / 20  -  train_rmse: 0.7791909151609235\n",
            "Epoch  11 / 20  -  train_rmse: 0.779186045858675\n",
            "Epoch  12 / 20  -  train_rmse: 0.779181672979482\n",
            "Epoch  13 / 20  -  train_rmse: 0.7791777292362436\n",
            "Epoch  14 / 20  -  train_rmse: 0.779174165072602\n",
            "Epoch  15 / 20  -  train_rmse: 0.7791709402014789\n",
            "Epoch  16 / 20  -  train_rmse: 0.7791680201336586\n",
            "Epoch  17 / 20  -  train_rmse: 0.7791653745723237\n",
            "Epoch  18 / 20  -  train_rmse: 0.7791629765625377\n",
            "Epoch  19 / 20  -  train_rmse: 0.7791608019704597\n",
            "Epoch  20 / 20  -  train_rmse: 0.7791588291188216\n",
            "\n",
            "Test RMSE: 0.9808\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coeJud_LfYGb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}