DUoR: Dynamic User-oriented re-Ranking calibration strategy for popularity bias treatment of recommendation

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dc.authorid0000-0003-3818-6712
dc.authorid0000-0001-5391-7371
dc.contributor.authorGulsoy, Mert
dc.contributor.authorYalcin, Emre
dc.contributor.authorTacli, Yucel
dc.contributor.authorBilge, Alper
dc.date.accessioned2026-01-24T12:31:11Z
dc.date.available2026-01-24T12:31:11Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractRecommender systems are widely used to provide personalized recommendations to users to help them navigate the vast amount of available content. They have become pervasive in various online applications. However, they often suffer from popularity bias, where popular items receive more recommendations, leading to potential issues such as limited diversity, homogenized user experience, perpetuating existing inequalities, and filter bubble effects. In this paper, we propose a novel approach to mitigate popularity bias by incorporating users' inclination towards item popularity. The proposed method incorporates a practical popularity inclination measuring strategy considering the dynamic preference tendencies of individuals to capture their unique propensities towards item popularity better and to provide more calibrated referrals regarding expectations of individuals on item popularity. Experimental results on benchmark datasets demonstrate that our proposed method effectively mitigates popularity bias by generating more diverse and balanced recommendations compared to several benchmark post-processing methods and outperforming them in diversity and fairness metrics according to the Borda count system. Overall, the proposed method presents a promising approach to addressing popularity bias in recommender systems by incorporating users' inclination towards item popularity and opens up potential directions for further research in the field.
dc.description.sponsorshipScientific and Technical Research Council of Turkey (TUBITAK) [122E040]
dc.description.sponsorshipAcknowledgments This study is supported in part by the Scientific and Technical Research Council of Turkey (TUBITAK) under grant number 122E040.
dc.identifier.doi10.1016/j.ijhcs.2025.103578
dc.identifier.issn1071-5819
dc.identifier.issn1095-9300
dc.identifier.scopus2-s2.0-105010688323
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ijhcs.2025.103578
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5710
dc.identifier.volume203
dc.identifier.wosWOS:001534250600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcademic Press Ltd- Elsevier Science Ltd
dc.relation.ispartofInternational Journal of Human-Computer Studies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectRecommender systems
dc.subjectPopularity bias
dc.subjectCalibrated recommendations
dc.subjectUser-oriented
dc.titleDUoR: Dynamic User-oriented re-Ranking calibration strategy for popularity bias treatment of recommendation
dc.typeArticle

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