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.
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.
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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.
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Language
Python
License
MIT
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Last pushed
Sep 11, 2025
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