Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation

Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR field, graph-based approaches are a relatively powerful technique, which generally extract item information by message aggregation under Euclidean space. However, such methods cannot effectively extract the hierarchical information contained among consecutive items in a session, which is critical to represent users' preferences. In this paper, we present a hyperbolic contrastive graph recommender (HCGR), a principled session-based recommendation framework involving Lorentz hyperbolic space to efficiently capture the coherence and hierarchical representations of the items. Within this framework, an adaptive hyperbolic attention computation is designed to aggregate the graph message of each user's preference in a session-based behavior sequence. In addition, a contrastive ranking loss with the hyperbolic distance is used to separate the positive and negative items. Compared with the Euclidean distance, the advantage of the hyperbolic distance in the contrastive ranking loss is analyzed. The results of extensive experiments on four real-world datasets demonstrate that HCGR consistently outperforms state-of-the-art baselines by 0.43 $\%$ -14.44 $\%$ in terms of $HitRate$ , $NDCG$ and $MRR$ .