A Novel Pre-Processing Technique to Combat Popularity Bias in Personality-Aware Recommender Systems

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dc.authorid0000-0001-7141-7119
dc.authorid0000-0003-3818-6712
dc.authorid0009-0004-1712-3151
dc.authorid0000-0003-3467-9915
dc.contributor.authorWaris, Madiha
dc.contributor.authorZaman Fakhar, Muhammad
dc.contributor.authorGulsoy, Mert
dc.contributor.authorYalcin, Emre
dc.contributor.authorBilge, Alper
dc.date.accessioned2026-01-24T12:29:00Z
dc.date.available2026-01-24T12:29:00Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractRecommender systems aid users in discovering items of interest across various domains. However, these systems often suffer from popularity bias, disproportionately recommending popular items and neglecting less popular ones that may still appeal to users. We propose an efficient pre-processing technique to mitigate popularity bias in personality-aware recommender systems, which leverage users' personality traits to enhance personalization and deliver higher-quality recommendations. Our method infuses synthetic ratings into less popular items, increasing their visibility in the recommendation process. We evaluate this technique using both accuracy and beyond-accuracy metrics but recognize that these metrics alone do not fully capture a recommender system's performance. To provide a holistic evaluation, we introduce the General Performance Indicator-a comprehensive metric combining accuracy and beyond-accuracy measures. Experimental results on two publicly available real-world datasets demonstrate that while our approach may cause minor decreases in accuracy, it significantly improves the beyond-accuracy aspects of recommendation quality. These enhancements underscore the effectiveness of our method in delivering a more balanced and diverse set of recommendations, addressing the limitations of traditional accuracy-focused approaches.
dc.identifier.doi10.1109/ACCESS.2024.3510475
dc.identifier.endpage183251
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85211738765
dc.identifier.scopusqualityQ1
dc.identifier.startpage183230
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3510475
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5064
dc.identifier.volume12
dc.identifier.wosWOS:001375719200015
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectRecommender systems
dc.subjectTail
dc.subjectRandom access memory
dc.subjectMeasurement
dc.subjectAccuracy
dc.subjectFiltering
dc.subjectSystematics
dc.subjectElectronic commerce
dc.subjectVectors
dc.subjectUser experience
dc.subjectPersonality aware recommender systems
dc.subjectpre-processing
dc.subjectpopularity bias problem
dc.subjectpopularity debiasing
dc.subjectsynthetic rating injection
dc.titleA Novel Pre-Processing Technique to Combat Popularity Bias in Personality-Aware Recommender Systems
dc.typeArticle

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