Multi-Behavior Hypergraph Contrastive Learning for Session-Based Recommendation

Most current session-based recommendations model session sequences solely based on the user's target behavior, ignoring the user's hidden preferences in auxiliary behaviors. Additionally, they use ordinary graphs to model one-to-one item correlations in the current session and fail to leverage other sessions to learn richer higher-order item correlations. To address these issues, a multi-behavior hypergraph contrastive learning model for session-based recommendations is proposed. This model represents all the sessions as global hypergraphs according to two types of behavior sequences. It employs contrastive learning to obtain global item embeddings, which are further aggregated to generate a global session representation that captures higher-order correlations of items from all session perspectives. A novel local heterogeneous hypergraph is designed for the current session to capture higher-order correlations between items with different behaviors in the current session, thus enhancing the local session representation. Additionally, a novel self-supervised signal is created by constructing a multi-behavior line graph, enhancing the global session representation. Finally, the local session representation, global session representation, and global item embedding are used to learn the predicted interaction probability of each item. Extensive experiments are conducted on three real datasets, and the results demonstrate that the proposed model significantly improves recommendation accuracy.