Md-Emon-Hasan/BookSage-AI
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.
This system helps online booksellers or librarians recommend books to readers. It takes in a reader's past interactions with books and the details of many books, then outputs personalized book suggestions. Anyone managing a book catalog who wants to offer tailored recommendations to their audience would use this.
Use this if you need a flexible system that can recommend books based on what similar readers liked, or on the characteristics of books a reader already enjoys.
Not ideal if your recommendation needs extend beyond books, or if you require real-time content ingestion and model updates for extremely dynamic catalogs.
Stars
13
Forks
4
Language
Python
License
MIT
Category
Last pushed
Mar 02, 2026
Commits (30d)
0
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