Online book recommendation system

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Date

2025

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Department of Computer Science and Engineering

Abstract

With the growing number of books, finding relevant content is challenging. We developed an Online Book Recommendation System using a hybrid approach combining contentbased and collaborative filtering, ensuring personalized and diverse recommendations.

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