Movie Recommendation Systems

Overview

This repository contains a comprehensive collection of movie recommendation systems. The goal is to implement and explore various methods of recommending movies to users, including:

• Collaborative Filtering: Based on user-item interactions.
• Content-Based Filtering: Based on movie metadata (e.g., genre, cast, overview).
• Bayesian Rating: Combines multiple factors like user ratings and popularity.
• Future Methods: Planned additions include hybrid models, matrix factorization, deep learning-based recommenders, and more.

Table of Contents

• Overview
• Implemented Methods
• Usage
• Installation
• Contributing
• License

Implemented Methods

1. Collaborative Filtering

   • Uses user behavior (ratings) to recommend movies that similar users have liked.
   • Example: User-based and Item-based collaborative filtering.

2. Content-Based Filtering

   • Recommends movies similar to those the user has liked in the past, based on metadata.
   • Example: TF-IDF vectorization, cosine similarity.

3. Bayesian Rating

   • A simple yet effective approach combining various factors such as average rating and popularity.
   • Example: IMDB-style weighted rating.

Usage

To explore the recommendation systems:

1. Clone the repository:

   git clone https://github.com/your-username/movie-recommendation-systems.git
   cd movie-recommendation-systems