Collaborative fair-is-better filtering for implicit feedback

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dc.contributor.authorDong, Hoang V.en
dc.contributor.authorNguyen, Huu-Quangen
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.authorLe, Duc-Trongen
dc.date.accessioned2024-05-29T12:31:15Z
dc.date.available2024-05-29T12:31:15Z
dc.date.issued2024en
dc.description.abstractWith the wide adoption of recommender systems, fairness has increasingly become a critical topic in many applications, such as e-commerce, job search, and online entertainment. Collaborative filtering is susceptible to unfair recommendations for users from sensitive groups due to the non-negligible presence of biases. Whereas recent work mostly concerned fairness with the explicit ratings, we propose fairness-awareness recommendation models, referred to as CFBF, for implicit feedback (e.g., clicks, views, or purchases), which are ubiquitous in today’s context. This paper considers sensitive attributes, such as gender and age, in both disjoined and combined manners to investigate the models’ unfairness. We discuss several fairness metrics for implicit feedback recommendations based on Spearman’s rank correlation and Kendal Tau. Comprehensive experiments on Movielens and LastFM show that CFBF significantly improves user groups’ fairness with comparable and even better ranking performance.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDong, H. V., Nguyen, H.-Q., Nguyen, H. D. and Le, D.-T. (2024) ‘Collaborative fair-is-better filtering for implicit feedback’, 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2024), Seville, Spain, 11-13 September. Procedia Computer Science, 246, pp. 1498-1507. https://doi.org/10.1016/j.procs.2024.09.599en
dc.identifier.doihttps://doi.org/10.1016/j.procs.2024.09.599
dc.identifier.eissn1877-0509en
dc.identifier.endpage1507
dc.identifier.journaltitleProcedia Computer Scienceen
dc.identifier.startpage1498
dc.identifier.urihttps://hdl.handle.net/10468/15949
dc.identifier.volume246
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartof28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2024), Seville, Spain, 11-13 Septemberen
dc.rights© 2024, the Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectCollaborative filteringen
dc.subjectFairnessen
dc.subjectImplicit feedbacken
dc.titleCollaborative fair-is-better filtering for implicit feedbacken
dc.typeConference itemen
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