Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation

Chenyang Zhou, Guobing Zou, Shengxiang Hu, Hehe Lv, Liangrui Wu, Bofeng Zhang

Abstract: Group recommendation constitutes a burgeoning research focus in recommendation systems. Despite a multitude of approaches achieving satisfactory outcomes, they still fail to address two major challenges: 1) these methods confine themselves to capturing user preferences exclusively within groups, neglecting to consider user collaborative signals beyond groups, which reveal users’ potential interests; 2) they do not sufficiently take into account the impact of multiple factors on group decision-making, such as individual expertise and influence, and the group’s general preferences. To tackle these challenges, we propose a new model named DDGR (Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation), designed to capture representations addressing two aspects: member preferences and group preferences. DDGR consists of two components: 1) a dual-graph convolutional network that combines the benefits of both hypergraphs and graphs to fully explore member potential interests and collaborative signals; 2) a dual-view fusion strategy that accurately simulates the group negotiation process to model the impact of multiple factors from member and group view, which can obtain semantically rich group representations. Thorough validation on two real-world datasets indicates that our model significantly surpasses state-of-the-art methods.