Robustness of privacy-preserving collaborative recommenders against popularity bias problem

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dc.authorid0000-0003-3818-6712
dc.authorid0000-0003-3467-9915
dc.contributor.authorGulsoy, Mert
dc.contributor.authorYalcin, Emre
dc.contributor.authorBilge, Alper
dc.date.accessioned2026-01-24T12:26:49Z
dc.date.available2026-01-24T12:26:49Z
dc.date.issued2023
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractRecommender systems have become increasingly important in today's digital age, but they are not without their challenges. One of the most significant challenges is that users are not always willing to share their preferences due to privacy concerns, yet they still require decent recommendations. Privacy-preserving collaborative recommenders remedy such concerns by letting users set their privacy preferences before submitting to the recommendation provider. Another recently discussed challenge is the problem of popularity bias, where the system tends to recommend popular items more often than less popular ones, limiting the diversity of recommendations and preventing users from discovering new and interesting items. In this article, we comprehensively analyze the randomized perturbation-based data disguising procedure of privacy-preserving collaborative recommender algorithms against the popularity bias problem. For this purpose, we construct user personas of varying privacy protection levels and scrutinize the performance of ten recommendation algorithms on these user personas regarding the accuracy and beyond-accuracy perspectives. We also investigate how well-known popularity-debiasing strategies combat the issue in privacy-preserving environments. In experiments, we employ three well-known real-world datasets. The key findings of our analysis reveal that privacy-sensitive users receive unbiased and fairer recommendations that are qualified in diversity, novelty, and catalogue coverage perspectives in exchange for tolerable sacrifice from accuracy. Also, prominent popularity-debiasing strategies fall considerably short as provided privacy level improves.
dc.description.sponsorshipScientific and Technical Research Council of Turkey (TUBITAK) [122E040]
dc.description.sponsorshipFunding This study is supported by the Scientific and Technical Research Council of Turkey (TUBITAK) under grant number 122E040. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.identifier.doi10.7717/peerj-cs.1438
dc.identifier.issn2376-5992
dc.identifier.pmid37547423
dc.identifier.scopus2-s2.0-85167867492
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.1438
dc.identifier.urihttps://hdl.handle.net/20.500.12868/4956
dc.identifier.volume9
dc.identifier.wosWOS:001023857800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPeerj Inc
dc.relation.ispartofPeerj Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectRecommender systems
dc.subjectPopularity bias
dc.subjectPrivacy-preserving
dc.subjectCollaborative filtering
dc.subjectUnfairness
dc.titleRobustness of privacy-preserving collaborative recommenders against popularity bias problem
dc.typeArticle

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