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content-based-filtering

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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.

  • Updated Jun 28, 2026
  • Python

Full-stack hybrid book recommendation system combining Collaborative Filtering and Content-Based Filtering with weighted hybrid scoring, modular data pipelines, and model persistence. Deployed via Flask with responsive HTML/CSS UI and integrated CI/CD for production-ready, scalable, and interactive recommendations.

  • Updated Jul 3, 2026
  • Python

🎬 Reelevance — a content-based movie recommender (TF-IDF + cosine similarity) on the TMDB 5000 dataset, with clustering, an honest evaluation vs. baselines, and an interactive CLI.

  • Updated May 30, 2026
  • Python

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