Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6, Issue 5 (September-October 2025) Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 5
September-October 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

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

Author(s) Ms. S P Preethi, Dr. Murthy SVN
Country India
Abstract In the digital era, recommender systems have become essential tools for providing users with personalized content tailored to their preferences. This paper presents a machine learning–based book recommendation system leveraging Collaborative Filtering (CF) techniques to enhance the reading experience by suggesting books aligned with users’ interests. The proposed system implements both User-Based and Item-Based Collaborative Filtering, as well as similarity-based measures such as Jaccard Similarity, to identify patterns in user ratings and generate accurate recommendations. By analyzing users’ past interactions and comparing them with the preferences of similar readers, the system overcomes challenges related to sparsity, cold start, and scalability inherent in traditional recommendation methods. Experimental evaluation demonstrates that the proposed approach improves recommendation accuracy and relevance, thereby providing an effective and personalized pathway for literary discovery. This work contributes to the field by integrating advanced CF techniques into book recommendation platforms, offering readers a seamless and insightful selection process.
Keywords Recommeder system,collaborative filterings,machine learning,presonalization
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 5, September-October 2025
Published On 2025-09-19
DOI https://doi.org/10.63363/aijfr.2025.v06i05.1365
Short DOI https://doi.org/g938mm
View / Download PDF File

Share this



E-ISSN 3048-7641
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

AIJFR DOI prefix is
10.63363/aijfr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJFR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-91574-37535 editor@aijfr.com