EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems
Recommender 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...
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PeerJ Inc.
2025-07-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-3055.pdf |
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| author | Mert Gulsoy Emre Yalcin Alper Bilge |
| author_facet | Mert Gulsoy Emre Yalcin Alper Bilge |
| author_sort | Mert Gulsoy |
| collection | DOAJ |
| description | Recommender 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. |
| format | Article |
| id | doaj-art-91917b455a784dffbb04d2346294c833 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-91917b455a784dffbb04d2346294c8332025-08-20T03:35:33ZengPeerJ Inc.PeerJ Computer Science2376-59922025-07-0111e305510.7717/peerj-cs.3055EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systemsMert Gulsoy0Emre Yalcin1Alper Bilge2Distance Education Research Center, Alaaddin Keykubat University, Antalya, TurkeyComputer Engineering Department, Sivas Cumhuriyet University, Sivas, TurkeyComputer Engineering Department, Akdeniz University, Antalya, TurkeyRecommender 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.https://peerj.com/articles/cs-3055.pdfRecommender systemsPopularity-debiasingPre-processingSynthetic rating injectionFairness |
| spellingShingle | Mert Gulsoy Emre Yalcin Alper Bilge EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems PeerJ Computer Science Recommender systems Popularity-debiasing Pre-processing Synthetic rating injection Fairness |
| title | EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems |
| title_full | EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems |
| title_fullStr | EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems |
| title_full_unstemmed | EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems |
| title_short | EquiRate: balanced rating injection approach for popularity bias mitigation in recommender systems |
| title_sort | equirate balanced rating injection approach for popularity bias mitigation in recommender systems |
| topic | Recommender systems Popularity-debiasing Pre-processing Synthetic rating injection Fairness |
| url | https://peerj.com/articles/cs-3055.pdf |
| work_keys_str_mv | AT mertgulsoy equiratebalancedratinginjectionapproachforpopularitybiasmitigationinrecommendersystems AT emreyalcin equiratebalancedratinginjectionapproachforpopularitybiasmitigationinrecommendersystems AT alperbilge equiratebalancedratinginjectionapproachforpopularitybiasmitigationinrecommendersystems |