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September 21, 2025Open Access

Machine Learning Based Personalized Recommendation Engine for Book Rent using Collaborative Filterings

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Authors

SPS P PreethiMSMurthy SVN

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Overview

This analysis demonstrates improved recommendation accuracy using collaborative filtering techniques in book recommendations.

Key Points

  • The machine learning-based system significantly improves recommendation relevance and accuracy for personalized book suggestions.
  • User-based and item-based collaborative filtering methods enhance the user experience, utilizing metrics like Jaccard similarity.
  • Evaluative experiments support the efficacy of the proposed approach against traditional methods, addressing issues like sparsity and cold start.
  • The integration of advanced collaborative filtering techniques into book platforms can revolutionize readers’ literary discovery methods.

Cite This Study

Preethi et al. (2025) studied this question.

synapsesocial.com/papers/68d46cbf31b076d99fa68853https://doi.org/10.63363/aijfr.2025.v06i05.1365
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A study on Personalized Recommendation System: Collaborative Filtering Combined with Machine Learning Algorithms2024
  2. 2Bookshelf: A Book Recommendation System Using Collaborative Filtering2024
  3. 3A Novel Framework on Book-Recommendation System2024
  4. 4A Machine Learning Approach to Movie Recommendation Systems2026
  5. 5Personalized Book Recommendations: A Hybrid Approach Leveraging Collaborative Filtering, Association Rule Mining, and Content-Based Filtering2024