Contrastive Hypergraph Flows With Multifaceted Gates for Cross-Domain Sequential Recommendation

Recommender systems play an important role in online platforms to provide personalized high-quality service for users. Sequential Recommendation (SR) captures users' dynamic preferences by modeling historical sequences, but most SR models suffer performance degradation due to data sparsity problem. To address this issue, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to transfer sequential patterns across domains to promote accuracy of single-domain recommendation services. Challenges arise when tackling CDSR, i.e., (1) how to extract intra- and inter-sequence collaborations within domains, (2) how to transfer stable interaction patterns across domains, and (3) how to alleviate data sparsity after integrating cross-domain knowledge. To this end, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CHFMG</b>, a contrastive hypergraph flow model with multifaceted gates, which contains two modules, i.e., dual hypergraph flow modeling and multi-view contrastive learning. The first module develops a dual hypergraph flow network to explore dynamic intra- and inter-domain sequential patterns. Innovative multifaceted attentive transfer gates connect local and global hypergraph flow, realizing internal feature fusion and external feature alignment in transfer. The second module achieves contrastive learning from two aspects, i.e., short-term contrasting for retaining sequential pattern consistency and long-term contrasting for enhancing distribution uniformity. Empirical studies on benchmark datasets demonstrate the effectiveness of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CHFMG</b>.