Movie Recommendation System

Overview

A movie recommendation system developed using Angular for the frontend, Spring Boot for the backend, and Python (FastAPI) for generating recommendations using a machine learning model (KNN). The system also features sentiment analysis to classify user reviews and provide robust recommendations based on user preferences. The system utilizes Kafka for event-driven architecture and Redis for caching user preferences to reduce response latency. MySQL is used as the database.

Technologies Used

• Frontend: Angular
• Backend: Spring Boot
• Recommendation Engine: Python (FastAPI), KNN Machine Learning Model
• Sentiment Analysis: Machine Learning Model ( classification of user reviews as positive or negative)
• Event-Driven Architecture: Kafka
• Caching: Redis
• Database: MySQL
• Security: JWT (JSON Web Tokens)

System Architecture

The system consists of three main components:

1. Angular Frontend: Handles user interactions and displays movie recommendations.
2. Spring Boot Backend: Handles user requests, publishes events to Kafka, and interacts with the database.
3. Python (FastAPI) Recommendation Engine: Consumes events from Kafka, updates user preferences in Redis, and generates movie recommendations using a KNN machine learning model.

How it Works

1. User Interaction: A user interacts with the Angular frontend, rating or liking movies.
2. Event Publishing: The Spring Boot backend publishes events to Kafka, which are then consumed by the Python recommendation engine.
3. User Preference Update: The Python recommendation engine updates the user's preferences in Redis.
4. Movie Recommendation: The Python recommendation engine generates movie recommendations based on the user's preferences and the KNN machine learning model.
5. Sentiment Analysis: The system classifies user reviews as positive or negative, which helps to improve the accuracy of the recommendations.

Features

• Personalized Movie Recommendations: Based on user preferences and behavior.
• Sentiment Analysis: Classifies user reviews as positive or negative.
• Event-Driven Architecture: Uses Kafka to handle user events and update user preferences in real-time.
• Caching: Uses Redis to reduce response latency and improve system performance.

Contributions are welcome! If you'd like to contribute to this project, please fork the repository and submit a pull request.

Author : Mohamed EL AFIA