Movie Recommender System 🎬

This repository contains a movie recommender system developed as part of an internship project in collaboration with IBM. The system uses collaborative filtering and long-tail recommendation techniques to provide personalized movie suggestions.

Project Overview

The goal of this project is to create a recommender system that goes beyond popular choices by including long-tail (less popular) items. The notebook demonstrates how to build a recommendation engine that balances user preferences with a diverse range of movies.

Features

• Collaborative Filtering: Recommends movies based on user interactions, using patterns in user preferences.
• Long-Tail Recommendations: Includes lesser-known movies to diversify recommendations and expose users to new content.

Getting Started

1. Clone the Repository:

   git clone https://github.com/Gitcomplex/Movie-Recommendation-system.git
   cd movie-recommender-system

2. Run the Notebook: Open Jupyter Notebook and launch the notebook file:

  jupyter notebook recommender-sys.ipynb

Notebook Contents

-> The notebook includes:

1. Data Loading: Imports user-movie interaction data.
2. Data Preprocessing: Cleans and prepares data for collaborative filtering.
3. Modeling: Implements collaborative filtering and integrates long-tail          recommendations.
4. Evaluation: Evaluates the model’s accuracy and diversity of recommendations.

Techniques Used

• Collaborative Filtering: Identifies patterns in user behavior for personalized suggestions.
• Long-Tail Strategy: Balances popular and niche movies in recommendations.

Future Work

• Adding content-based filtering to improve recommendation quality.
• Integrating hybrid recommendation approaches for a more robust system.

Acknowledgments

This project was developed as part of an internship with IBM.