Disentangled Modeling of Preferences and Social Influence for Group Recommendation

• URL: http://arxiv.org/abs/2501.11342v1
• Date: Mon, 20 Jan 2025 09:03:18 GMT
• Title: Disentangled Modeling of Preferences and Social Influence for Group Recommendation
• Authors: Guangze Ye, Wen Wu, Guoqing Wang, Xi Chen, Hong Zheng, Liang He,
• Abstract summary: Group recommendation (GR) aims to suggest items for a group of users in social networks. Existing methods either neglect the social influence of individual members or bundle preferences and social influence together. We propose a novel model based on Disentangled Modeling of Preferences and Social Influence for Group Recommendation (DisRec)
• Score: 16.953172230355268
• License:
• Abstract: The group recommendation (GR) aims to suggest items for a group of users in social networks. Existing work typically considers individual preferences as the sole factor in aggregating group preferences. Actually, social influence is also an important factor in modeling users' contributions to the final group decision. However, existing methods either neglect the social influence of individual members or bundle preferences and social influence together as a unified representation. As a result, these models emphasize the preferences of the majority within the group rather than the actual interaction items, which we refer to as the preference bias issue in GR. Moreover, the self-supervised learning (SSL) strategies they designed to address the issue of group data sparsity fail to account for users' contextual social weights when regulating group representations, leading to suboptimal results. To tackle these issues, we propose a novel model based on Disentangled Modeling of Preferences and Social Influence for Group Recommendation (DisRec). Concretely, we first design a user-level disentangling network to disentangle the preferences and social influence of group members with separate embedding propagation schemes based on (hyper)graph convolution networks. We then introduce a socialbased contrastive learning strategy, selectively excluding user nodes based on their social importance to enhance group representations and alleviate the group-level data sparsity issue. The experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two realworld datasets.

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