Multidimensional diversity in video recommender systems: A holistic framework of literature gaps and future directions

Basit Öğe Kaydı
dc.authorid0000-0002-8344-1180
dc.authorid0000-0003-2960-8725
dc.contributor.authorDönmez, İlknur
dc.contributor.authorBulut, Faruk
dc.date.accessioned2026-10-13T11:37:28Z
dc.date.available2026-10-13T11:37:28Z
dc.date.issued2026
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractVideo recommender systems shape content discovery, user engagement, and information access across digital platforms such as YouTube, Netflix, and TikTok. Although recent advances in deep learning, graph-based models, and multimodal recommendation have improved personalization, key challenges remain, including limited di versity metrics, weak geometric analysis of embedding spaces, inadequate multimodal datasets, and insufficient integration of fairness and transparency. This review synthesizes recent research on diversity-aware video recommender systems through a structured literature review. The screening process identified 572 records from six scholarly sources; after title–abstract screening and full-text eligibility assessment, 101 studies were included in the qualitative synthesis. The findings show that diversity is often treated as a narrow, one-dimensional objective, while broader system-level, user-centered, and societal perspectives remain underdeveloped. The re view therefore argues for multidimensional and human-centered recommendation frameworks that incorporate geometric diversity modeling, multi-objective optimization, long-term user satisfaction, and user-controllable design. The study provides a conceptual basis for developing more robust, fair, transparent, and sustainable video recommender systems.
dc.description.sponsorshipThe Open Access publication of this article was funded by the University of Essex. Bu makalenin Açık Erişim olarak yayımlanması, Essex Üniversitesi tarafından finanse edilmiştir.
dc.identifier.citationDönmez, İ., & Bulut, F. (2026). Multidimensional diversity in video recommender systems: A holistic framework of literature gaps and future directions. Intelligent Systems with Applications, 31, pp. 1-12. https://doi.org/10.1016/j.iswa.2026.200724
dc.identifier.doi10.1016/j.iswa.2026.200724
dc.identifier.endpage12
dc.identifier.issn2667-3053
dc.identifier.scopus2-s2.0-105048682137
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1016/j.iswa.2026.200724
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1668
dc.identifier.volume31
dc.identifier.wosWOS:001863839400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.indekslendigikaynak.otherESCI - Emerging Sources Citation Index
dc.institutionauthorDönmez, İlknur
dc.institutionauthorid0000-0002-8344-1180
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofIntelligent Systems with Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectVideo Recommendation Systems (VRS)
dc.subjectLiterature Review
dc.subjectDiversity
dc.subjectBias
dc.subjectMulti-Objective Optimization
dc.subjectHuman-Centered
dc.subjectVRS
dc.titleMultidimensional diversity in video recommender systems: A holistic framework of literature gaps and future directions
dc.typeArticle
dspace.entity.typePublication

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