About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
IoT 23(1):

Editorial

Personalized Book Recommendations: A Hybrid Approach Leveraging Collaborative Filtering, Association Rule Mining, and Content-Based Filtering

Download119 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eetiot.6996,
        author={Akash Bhajantri and Nagesh K and R. H. Goudar and Dhananjaya G M and Rohit.B. Kaliwal and Vijayalaxmi Rathod and Anjanabhargavi Kulkarni and Govindaraja K},
        title={Personalized Book Recommendations: A Hybrid Approach Leveraging Collaborative Filtering, Association Rule Mining, and Content-Based Filtering},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2024},
        month={12},
        keywords={Content-based filtering, Collaborative filtering, Book recommendation system},
        doi={10.4108/eetiot.6996}
    }
    
  • Akash Bhajantri
    Nagesh K
    R. H. Goudar
    Dhananjaya G M
    Rohit.B. Kaliwal
    Vijayalaxmi Rathod
    Anjanabhargavi Kulkarni
    Govindaraja K
    Year: 2024
    Personalized Book Recommendations: A Hybrid Approach Leveraging Collaborative Filtering, Association Rule Mining, and Content-Based Filtering
    IOT
    EAI
    DOI: 10.4108/eetiot.6996
Akash Bhajantri1, Nagesh K1, R. H. Goudar1,*, Dhananjaya G M1, Rohit.B. Kaliwal1, Vijayalaxmi Rathod1, Anjanabhargavi Kulkarni1, Govindaraja K2
  • 1: Visvesvaraya Technological University
  • 2: Sainik School Kodagu
*Contact email: rhgoudar.vtu@gmail.com

Abstract

Well over ten years already, recommender systems have been in use. Many people have perpetually grappled with synonymous with selecting what to read next. The choice of a textbook or reference book to read on a subject they are unaware of might be difficult for even students. Nowadays, people can go into a library or browse the internet without having a specific book in mind. But each reader is different, in their tastes and interests. In today's information-rich world, Essential tools like recommendation systems play a pivotal role in simplifying the lives of consumers. For book lovers, the Book Recommendation Sys- tem(BRS) is the ideal fix for readers. Online bookstores are competing for attention, but current systems extract unnecessary data and result in low user satisfaction, this author crafted the BRS, merging collaborative filtering(CF), association rule mining(arm), and content-based filtering. BRS delivers recommendations that are both efficient and effective. Concept papers primary intention encourage a love of reading and help people form lifelong habits. BRS selects an ideal book based on a reader's preferences and data from various sources, inspiring individuals to read more and discover new authors and genres. Leveraging Information sets and machine learning algorithms, collaborative filtering and content filtering techniques are used to help people find the perfect book that fascinates and incites a desire to explore additional literary treasures.

Keywords
Content-based filtering, Collaborative filtering, Book recommendation system
Received
2024-12-05
Accepted
2024-12-05
Published
2024-12-05
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.6996

Copyright © 2024 R H. Goudar et al, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL