Movie Recommendation Systems

Overview

This repository provides practical implementations, detailed explanations, and comparisons of these methods to offer insights into building effective 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