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

• URL: http://arxiv.org/abs/2405.16772v1
• Date: Mon, 27 May 2024 02:45:01 GMT
• 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,
• Abstract summary: Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. Existing social recommendation models fail to address the issues of popularity bias and the redundancy of social information. We propose a Condition-Guided Social Recommendation Model (named CGSoRec) to mitigate the model's popularity bias.
• Score: 64.73474454254105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 fail to address the issues of popularity bias and the redundancy of social information, as they directly characterize social influence across the entire social network without making targeted adjustments. 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. The code is in: https://github.com/hexin5515/CGSoRec.

Related papers

• RecDiff: Diffusion Model for Social Recommendation [14.514770044236375]
  We propose a novel diffusion-based social denoising framework for recommendation (RecDiff) By performing multi-step noise diffusion and removal, RecDiff possesses a robust ability to identify and eliminate noise from encoded user representations. The results demonstrate its superiority in terms of recommendation accuracy, training efficiency, and denoising effectiveness.
  arXiv  Detail & Related papers  (2024-06-01T10:20:52Z)
• Learning Social Graph for Inactive User Recommendation [50.090904659803854]
  LSIR learns an optimal social graph structure for social recommendation, especially for inactive users. Experiments on real-world datasets demonstrate that LSIR achieves significant improvements of up to 129.58% on NDCG in inactive user recommendation.
  arXiv  Detail & Related papers  (2024-05-08T03:40:36Z)
• Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation [12.120586712440673]
  Social recommendation systems face the problem of social influence bias, which can lead to an overemphasis on recommending items that friends have interacted with. We propose a Causal Disentanglement-based framework for Regulating Social influence Bias in social recommendation, named CDRSB, to improve recommendation performance.
  arXiv  Detail & Related papers  (2024-03-06T09:48:48Z)
• Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community [63.949893724058846]
  Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content. This work pioneers a paradigm shift, unveiling Social Reward - an innovative reward modeling framework. We embark on an extensive journey of dataset curation and refinement, drawing from Picsart: an online visual creation and editing platform.
  arXiv  Detail & Related papers  (2024-02-15T10:56:31Z)
• IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation [25.499653256726926]
  Socially connected users are assumed to share similar preferences, enhancing recommendation accuracy and addressing cold start issues. Our statistical analysis indicates a significant amount of noise in the social network, where many socially connected users do not share common interests. We propose an innovative underlineInterest-aware underlineDenoising and underlineView-guided underlineTuning (IDVT) method for the social recommendation.
  arXiv  Detail & Related papers  (2023-08-30T10:03:55Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• Federated Social Recommendation with Graph Neural Network [69.36135187771929]
  We propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. We devise a novel framework textbfFedrated textbfSocial recommendation with textbfGraph neural network (FeSoG)
  arXiv  Detail & Related papers  (2021-11-21T09:41:39Z)
• Using Social Media Background to Improve Cold-start Recommendation Deep Models [8.444156978118087]
  We investigate whether social media background can be used as extra contextual information to improve recommendation models. Based on an existing deep neural network model, we proposed a method to represent temporal social media background as embeddings. The results show that our method of fusing social media background with the existing model does generally improve recommendation performance.
  arXiv  Detail & Related papers  (2021-06-04T04:46:29Z)
• SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems [29.24459965940855]
  We present a novel deep learning model SocialTrans for social recommendations. The first module is based on a multi-layer Transformer to model users' personal preference. The second module is a multi-layer graph attention neural network (GAT), which is used to model the social influence strengths between friends in social networks. The last module merges users' personal preference and socially influenced preference to produce recommendations.
  arXiv  Detail & Related papers  (2020-05-09T03:39:45Z)
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation [50.08581302050378]
  Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences. We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet) In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
  arXiv  Detail & Related papers  (2020-01-15T08:45:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.