Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation

Session-Based Recommendation(SBR) aims to capture users’ short-term and dynamic preferences through anonymous sessions. Most existing SBR methods neglect the collaborative information between multiple behaviors in a session when modeling user preferences, and they often struggle to capture the complex correlations of contextual information across sessions, which can lead to poor recommendation performance. To address these issues, this paper proposes a Self-Supervised enhancement method for Multi-Behavior Session-based Recommendation(SSMB-SR). SSMB-SR represents the session sequence using a heterogeneous graph to capture intricate behavior interactions and a hypergraph for contextual information integration. Specifically, we designed a heterogeneous enhancement module that deeply understands the intrinsic connections of behaviors and the interdependencies between different behavior types by enhancing the behavioral information of the central node, effectively capturing the complex dynamic interactions between nodes within the session to obtain accurate item embeddings. Concurrently, we propose a self-supervised training method for the module that mitigates location bias and minimizes the impact of noisy behaviors. For cross-session, we combine relevant contextual information through a hypergraph to achieve accurate recommendation results. Experimental results show that our proposed self-supervised enhancement method significantly improves recommendation performance and has a better performance compared to recommendation methods that only consider a single behavior.