HGRec: Group Recommendation With Hypergraph Convolutional Networks

Abstract

Recommendation systems have shifted from personalization for individual users to consensus for groups as a result of people's growing tendency to join groups to participate in various everyday activities, like family meals and workplace reunions. This is because social networks have made it easier for people to participate in these kinds of events. Group recommendation is the process of suggesting items to groups. To derive group preferences, the majority of current approaches combine the individual preferences of group members utilizing heuristic or attention mechanism-based techniques. These approaches, however, have three issues. First, these approaches ignore the complex high-order interactions that occur both inside and outside of groups, just modeling the preferences of individual groups of users. Second, a group's ultimate decision is not always determined by the members’ preferences. Nevertheless, current approaches are not adequate to represent such preferences across groups. Last, data sparsity affects group recommendations due to the sparsity of group–item interactions. To overcome the aforementioned constraints, we propose employing hypergraph convolutional networks for group recommendation. Specifically, our design aims to achieve excellent group preferences by establishing a high-order preference extraction view represented by the hypergraph, a consistent preference extraction view represented by the overlap graph, and a conventional preference extraction view represented by the bipartite graph. The linkages between the three various views are then established by using cross-view contrastive learning, and the information between different views can be complementary, thereby improving each other. Comprehensive experiments on three publicly available datasets show that our method performs better than the state-of-the-art baseline.