基于双图混合随机游走的社会化推荐模型

In recent years, social recommendation approaches have attracted attention because they can effectively improve the recommendation quality when user-item interaction data is sparse. Explicit and implicit social relations, as aux⁃ iliary information, are used to improve the recommendation quality. However, social relations are represented by simple graphs in existing models. The nature of edges connecting pair-wise nodes in simple graphs makes it suitable for describing explicit relations. Still, it is incapable of modeling complex implicit relations, such as the collective relation between multi⁃ ple users who have purchased the same product. Therefore, it isn't easy to learn the node representation accurately, only based on simple graphs, which even affects the recommender's performance. In this paper, we propose a recommendation model based on a bi-graph hybrid random walk (BG-Rec) to overcome this problem, which combines hypergraph and graph. We construct a hypergraph and a simple graph to depict complex implicit relations and explicit social relations sepa⁃ rately. Next, the mixed random walk strategy (MixRandom) is used to generate node sequences that combine implicit and explicit relations. Furthermore, node sequences are used for learning more accurate representations of nodes. Then, positive feedback hypergraph and negative feedback hypergraph are constructed based on user ratings, so that more fine-grained friend relations can be considered to identify reliable friends. Finally, the personalized ranking of items is optimized by con⁃ sidering the preferences of reliable friends and the maximization of the posterior probability. Experiments on three public datasets show the superiority of BG-Rec in recommendation performance. The cold-start study and ablation study validates the effectiveness of alleviating the cold-start problem and rationality of hypergraph modeling.