Major Project: Recommender System with AI, Django, and Vue.js

This project is a full-stack implementation of a Recommender System using AI techniques for product recommendation. It combines a Django backend with machine learning models and a Vue.js frontend to provide personalized recommendations for users. The project includes features such as collaborative filtering, content-based filtering, and a hybrid recommendation system.

Table of Contents

• Project Overview
• Features
• Tech Stack
• Installation
• Usage
• API Endpoints
• Testing
• Future Enhancements
• License

Project Overview

This project was developed as part of the Major Project for demonstrating the integration of machine learning models with a full-stack web application. The goal of the system is to provide recommendations to users based on their interactions, reviews, and product preferences. The system uses collaborative filtering, content-based filtering, and a hybrid approach to make personalized suggestions.

Features

• User Authentication: Users can register, log in, and manage their profiles.
• Collaborative Filtering: Recommends items based on user similarity.
• Content-Based Filtering: Recommends items based on the product features.
• Hybrid Recommender: Combines collaborative and content-based filtering techniques.
• AI Models: Machine learning models are trained and deployed for recommendations.
• Real-Time Recommendations: Provides real-time product recommendations based on user behavior.
• API Integration: Frontend communicates with the Django API to fetch and display recommendations.

Tech Stack

• Backend: Django, Django Rest Framework (DRF)
• Frontend: Vue.js
• Database: PostgreSQL
• Machine Learning Models: Python (Scikit-learn, TensorFlow)
• Authentication: JWT (JSON Web Tokens)
• Deployment: Docker

Installation

Prerequisites

• Python 3.x
• Node.js
• PostgreSQL
• Docker (optional)

Backend Setup

1. Clone the repository:

   git clone https://github.com/atabekdemurtaza/MajorProject.git
   cd MajorProject/backend
   

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate
   pip install -r requirements/dev.txt
   

3. Set up the database (PostgreSQL) and update the config/settings.py file with your database credentials.

4. Run migrations:

   python manage.py migrate
   

5. Start the Django server:

   python manage.py runserver
   
   

Frontend Setup

1. Navigate to the frontend directory:

   cd ../frontend
   

2. Install dependencies:

   npm install
   

3. Start the Vue.js development server:

   npm run serve 
   

Running with Docker

docker-compose up --build

Usage

1. Open the frontend in your browser at http://localhost:8080/.
2. Register or log in to your account.
3. Browse products, leave reviews, and get personalized recommendations.
4. The recommender system will suggest products based on your preferences and interactions.

API Endpoints

The backend provides a set of API endpoints to interact with the recommender system.

Authentication:
    POST /api/auth/register/ - Register a new user
    POST /api/auth/login/ - Login and obtain JWT tokens
Recommendations:
    GET /api/products/ - Retrieve list of products
    GET /api/recommendations/ - Get product recommendations based on user behavior
Reviews:
    POST /api/reviews/ - Submit a review for a product

Refer to the API documentation (included in the project) for more details on available endpoints.

Testing

You can run unit tests for both the backend and frontend:

Backend (Django)

python manage.py test

Frontend (Vue)

npm run test

Full Scheme

Future Enhancements

Improve Recommendation Accuracy: Experiment with more advanced machine learning models.
Add Social Features: Allow users to share recommendations with friends.
Deployment: Improve deployment pipelines with CI/CD tools like GitHub Actions.
Mobile Compatibility: Enhance the frontend for mobile responsiveness