Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias

[2405.16772] Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Social and Information Networks arXiv:2405.16772 (cs) [Submitted on 27 May 2024 ( v1 ), last revised 10 Apr 2026 (this version, v2)] Title: Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias Authors: Xin He , Wenqi Fan , Ruobing Wang , Yili Wang , Ying Wang , Shirui Pan , Xin Wang View a PDF of the paper titled Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias, by Xin He and 6 other authors View PDF HTML (experimental) Abstract: Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplify the popularity bias in recommendation models, resulting in more frequent recommendation of hot items and fewer long-tail items, but also include a substantial amount of redundant information that is essentially meaningless for the model's performance. Existing social recommendation models often integrate the entire social network directly, with little effort to filter or adjust social information to mitigate popularity bias introduced by the social network. In this paper, we propose a Condition-Guided Social Recommendation Model (named CGSoRec) to mitigate the model's popularity bias by denoising the social network and adjusting the weights of user's social preferences. More specifically, CGSoRec first includes a Condition-Guided Social Denoising Model (CSD) to remove redundant social relations in the social network for capturing users' social preferences with items more precisely. Then, CGSoRec calculates users' social preferences based on denoised social network and adjusts the weights in users' social preferences to make them can counteract the popularity bias present in the recommendation model. At last, CGSoRec includes a Condition-Guided Diffusion Recommendation Model (CGD) to introduce the adjusted social preferences as conditions to control the recommendation results for a debiased direction. Comprehensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method. Comments: Accepted by Neural Networks Subjects: Social and Information Networks (cs.SI) ; Machine Learning (cs.LG) Cite as: arXiv:2405.16772 [cs.SI] (or arXiv:2405.16772v2 [cs.SI] for this version) https://doi.org/10.48550/arXiv.2405.16772 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1016/j.neunet.2025.107317 Focus to learn more DOI(s) linking to related resources Submission history From: Xin He [ view email ] [v1] Mon, 27 May 2024 02:45:01 UTC (7,745 KB) [v2] Fri, 10 Apr 2026 09:22:13 UTC (6,976 KB) Full-text links: Access Paper: View a PDF of the paper titled Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias, by Xin He and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SI < prev | next > new | recent | 2024-05 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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