Digitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles

Basit Öğe Kaydı
dc.authoridYalcin, Emre/0000-0003-3818-6712
dc.authoridARETA HIZIROGLU, OURANIA/0000-0001-8607-6089
dc.contributor.authorDogan, Onur
dc.contributor.authorYalcin, Emre
dc.contributor.authorHiziroglu, Ourania Areta
dc.date.accessioned2025-03-20T09:50:49Z
dc.date.available2025-03-20T09:50:49Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractPurposeReading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library's offerings, contributing to a holistic learning experience.Design/methodology/approachThis study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user's rating.FindingsThe comparative results indicated that users are quite satisfied with the recommendations generated by W\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.Originality/valueThe proposed W\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.
dc.description.sponsorshipIzmir Bakircay University; Smart University; Smart University and Digital Transformation Coordinatorship
dc.description.sponsorshipWe extend our heartfelt appreciation to Izmir Bakircay University, Smart University and Digital Transformation Coordinatorship (https://sudt.bakircay.edu.tr/) for their generous support, scholarly guidance, and access to their extensive resources, which greatly contributed to the success of this research.
dc.identifier.doi10.1108/LM-03-2024-0030
dc.identifier.endpage505
dc.identifier.issn0143-5124
dc.identifier.issn1758-7921
dc.identifier.issue8/9
dc.identifier.scopus2-s2.0-85200114548
dc.identifier.scopusqualityQ2
dc.identifier.startpage489
dc.identifier.urihttps://doi.org/10.1108/LM-03-2024-0030
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2328
dc.identifier.volume45
dc.identifier.wosWOS:001282904100001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd
dc.relation.ispartofLibrary Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250319
dc.subjectRecommendation systems
dc.subjectBook recommender
dc.subjectReading habits
dc.subjectConsumption-based genre profile
dc.subjectVote-based genre profile
dc.titleDigitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu