EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems

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dc.authorid0000-0003-3818-6712
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.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractRecommender systems often suffer from popularity bias problem, favoring popular items and overshadowing less known or niche content, which limits recommendation diversity and content exposure. The root reason for this issue is the imbalances in the rating distribution; a few popular items receive a disproportionately large share of interactions, while the vast majority garner relatively few. In this study, we propose the EquiRate method as a pre-processing approach, addressing this problem by injecting synthetic ratings into less popular items to make the dataset regarding rating distribution more balanced. More specifically, this method utilizes several synthetic rating injection and synthetic rating generation strategies: (i) the first ones focus on determining which items to inject synthetic ratings into and calculating the total number of these ratings, while (ii) the second ones concentrate on computing the concrete values of the ratings to be included. We also introduce a holistic and highly efficient evaluation metric, i.e., the FusionIndex, concurrently measuring accuracy and several beyond-accuracy aspects of recommendations. The experiments realized on three benchmark datasets conclude that several EquiRate's variants, with proper parameter-tuning, effectively reduce popularity bias and enhance recommendation diversity. We also observe that some prominent popularity-debiasing methods, when assessed using the FusionIndex, often fail to balance the referrals' accuracy and beyond-accuracy factors. On the other hand, our best-performing EquiRate variants significantly outperform the existing methods regarding the FusionIndex, and their superiority is more apparent for the high-dimension data collections, which are more realistic for real-world scenarios.
dc.description.sponsorshipScientific and Technical Research Council of Turkey (TUBITAK)
dc.description.sponsorshipIn the preparation of certain sections of this manuscript, the authors utilized ChatGPT to improve grammar, enhance clarity, and support language refinement. All content generated with the assistance of this tool was subsequently reviewed, revised, and approved by the authors, who take full responsibility for the final version of the manuscript.
dc.identifier.doi10.7717/peerj-cs.3055
dc.identifier.issn2376-5992
dc.identifier.pmid40989421
dc.identifier.scopus2-s2.0-105025460067
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.3055
dc.identifier.urihttps://hdl.handle.net/20.500.12868/4957
dc.identifier.volume11
dc.identifier.wosWOS:001556611800001
dc.identifier.wosqualityQ2
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-debiasing
dc.subjectPre-processing
dc.subjectSynthetic rating injection
dc.subjectFairness
dc.titleEquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems
dc.typeArticle

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