Intent Does Matter! Propagating High-order Relations for Exploring Interest Preferences

Session-based recommendation (SBR) aims to predict the user’s action at the next timestamp according to an anonymous yet short interaction sequence (i.e., session). Almost all the existing SBR solutions for user preference are only based on the current session without exploiting the high-order relations among other sessions, which may restrict the SBR representation ability and even deteriorate the performance. To this end, we propose a \textbf{Hy}per-\textbf{r}elation \textbf{a}lignment hyper\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork, called Hyra-GCN, for better inferring the user preference of the current session. Specifically, we first model session-based data as a hypergraph capable of representing high-order relationships to exploit item transitions over sessions in a more subtle manner. Subsequently, we explore self-supervised learning on item-session hypergraphs, so as to alleviate the problem of data sparsity. We further propose Hyperedge-to-Node (H2N) to learn hypergraph representation for neighbors to enhance supervised signals. Extensive experiments on multiple real-world datasets demonstrate the superiority of the proposed model over the state-of-art methods, and the results validate the effectiveness of graph modeling and self-supervised task.