Enhanced Pre-training for Recommendation via Hypergraph Structural Entropy

Research on recommender systems plays a crucial role in alleviating information overload amid the current proliferation of data while diminishing user decision-making and transaction costs within intricate environments. The prevailing recommendation models currently rely on graph-based methods, such as GCN, GAT, HGNN, and so on, which are constrained by the sparsity of training data and the underutilization of graph structures. In this work, we present EPRHSE, an E nhanced P re-training framework for R ecommendation based on H ypergraph S tructural E ntropy, which encodes the topology of the recommender system. We begin by designing two forms of pre-training tasks to capture the heterogeneous relationships among users or items. These pre-training tasks build multiple auxiliary task hypergraphs, compensate for the sparse interactions between users and items, and unveil latent information. Secondly, we introduce a new method for optimizing the hypergraph structure entropy. The method involves converting the hyperedge information in the hypergraph to form a high-dimensional encoding tree. Hypergraph structure entropy helps decode the essential structure of the recommendation bipartite graph and enables hierarchical clustering of users or items. Thirdly, we propose a hypergraph pooling training methodology incorporating pooling and unpooling layers into the hypergraph convolutional network to amalgamate high-order information. By transferring advanced community insights to primary users or items, the process of social diffusion is enhanced, consequently refining node embedding quality. Compared with 13 representative recommendation approaches on five real datasets, comprehensive experiments demonstrate the effectiveness and advantages of EPRHSE.