Heterogeneous Hypergraph Enhanced Trust Recommendation in Mobile Social Networks

With the widespread adoption of mobile devices, an increasing number of users are engaging in social interactions through these devices. Mobile social networks have thus emerged in response. Existing research has shown that utilizing mobile social relations can effectively enhance the performance of recommendation systems. However, most studies only exploit single social relations such as pairwise relations, overlooking the effect of high-order complexity of user relations which contain some potentially beneficial information. What's more, they ignore the impact of the trusters who provide some potential feedbacks in the mobile social network. Therefore, this paper proposes our framework H2TRec using hypergraph convolution in the pretraining stage to learn high-order neighbor information in the mobile social network and utilizing the metapath-based GAT to model users' bidirectional trust relations. First, mobile social communities are partitioned by random walk based on the fusion graph which consolidates all different nodes and relations. Second, each mobile social community is represented as a hyperedge to construct the hypergraph and the high-order neighbor prior knowledge is learned using hypergraph convolution. Third, implicit relations are mined and the metapath-based GAT is utilized to model the preferences of users and items. Notably, unlike previous work, we consider mobile users' out-degree and in-degree features, which enhance the user embeddings. Additionally, a loss term aiming to improve centrality is added to make the preference features of mobile social communities more prominent. Extensive experiments on five popular real-world datasets demonstrate that our H2TRec can improve precision compared with state-of-the-art methods.