AmirhosseinHonardoust/Movie-Recommendation-System

Movie recommendation system with Python. Implements content-based filtering (TF-IDF + cosine similarity), collaborative filtering with matrix factorization (TruncatedSVD), and a hybrid approach. Evaluates with Precision@K, Recall@K, and NDCG. Includes rating distribution plots, top movies, and sample recommendations.

24
/ 100
Experimental

This system helps you generate personalized movie recommendations for users, much like what streaming services do. You provide a list of movies and user ratings for those movies, and it outputs tailored movie suggestions for each user. It's designed for data scientists or product managers who want to understand or implement different recommendation strategies.

No commits in the last 6 months.

Use this if you need to quickly build and evaluate different movie recommendation algorithms, including content-based, collaborative, and hybrid approaches, and want to see how they perform.

Not ideal if you are looking for a ready-to-deploy, production-scale recommendation engine with a user interface, as this focuses on the underlying algorithms and evaluation.

movie-recommendations personalization user-engagement data-science product-management
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 7 / 25
Maturity 15 / 25
Community 0 / 25

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31

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Language

Python

License

MIT

Last pushed

Sep 11, 2025

Commits (30d)

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