Project Overview:

The Movie Recommendation System is a web-based platform that suggests movies to users based on their preferences, history, or behavior using machine learning algorithms. The system uses collaborative filtering, content-based filtering, or a hybrid approach to provide personalized movie suggestions.

Users can register, rate movies, and receive smart recommendations, enhancing their movie-watching experience. This project integrates data science, AI/ML, and web development.

Core Features:

1.  User Registration & Login

2.  Browse & Search Movies

3.  Rate & Review Movies

4.  Personalized Movie Recommendations

5.  Machine Learning-Based Recommendation Engine

6.  Watchlist and Movie History Tracking

7.  Admin Dashboard to View Popular Trends

Technology Stack:

Backend (choose one):

• PHP + MySQL

• Node.js + MongoDB

• Java (Spring Boot) + PostgreSQL

Frontend:

• HTML5, CSS3, Bootstrap

• JavaScript (AJAX, Chart.js)

Recommendation Engine (Python):

• Libraries: Pandas, Scikit-learn, Surprise, TensorFlow

• ML Models:

  • Content-based Filtering

  • Collaborative Filtering (User/Item based)

  • Hybrid Model

How It Works:

1. User Interaction:

   • User signs up and rates a few movies

   • System stores ratings and viewing data

2. ML Model Triggers:

   • Based on rated movies, ML model recommends unseen movies using:

     • Similar users' preferences (Collaborative Filtering)

     • Similar movie genres/actors/directors (Content-based)

3. Dashboard Displays:

   • Top picks for the user

   • Trending movies

   • Recommendations refreshed with more data