mdzaheerjk/Collaborative-Filtering-Recommendation-System
The End-to-End Recommender System is a machine learning-based application designed to recommend books to users based on collaborative filtering. The project encompasses a complete MLOps pipeline, including data ingestion, validation, transformation, model training, and a web-based user interface for interaction.
This system helps online bookstores or content platforms recommend books to their users. By analyzing past user interactions with books, it takes existing book ratings and preferences to suggest new books a user is likely to enjoy. This is for anyone managing an online book catalog who wants to enhance user engagement and sales through personalized recommendations.
Use this if you manage an online book platform and want to automatically suggest personalized book recommendations to individual users.
Not ideal if your recommendation needs extend beyond books or if you require advanced content-based filtering without user interaction data.
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MIT
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Last pushed
Jan 27, 2026
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