When Multi-Behavior Meets Multi-Interest: Multi-Behavior Sequential Recommendation with Multi-Interest Self-Supervised Learning

Sequential Recommendation utilizes interaction history to uncover users' dynamic interest changes and recommend the most relevant items for their next interaction. In recent years, multi-behavior modeling and multi-interest modeling have been hot research topics. Although multi-behavior and multi-interest methods have strengths in their respective domains, both have limitations. Multi-behavior methods focus excessively on target behavior recommendation (i.e., purchase) without sufficiently leveraging auxiliary behavior interactions (i.e., click) to discern users' multi-faced interests, leading to suboptimal recommendation quality. Meanwhile, existing multi-interest methods overlook the distinct user interests behind multi-behavior when extracting interests, resulting in inaccurate interest modeling. Combining the two can not only facilitate sophisticated modeling of complex user interests but also deepen understanding of multi-behavior interactions, achieving synergistic effects. In this paper, we propose a novel approach called Multi-Interest Self-Supervised Learning (MISSL) that precisely unifies multi-behavior and multi-interest modeling to obtain more comprehensive and accurate user profiles. MISSL utilizes a hypergraph transformer network to extract behavior-specific and shared interests followed by multi-interest self-supervised learning to refine item and interest representations. Additionally, a behavior-aware training task is incorporated to enhance model stability during training. Extensive experiments on benchmark datasets demonstrate that MISSL outperforms baseline methods. The source code for MISSL is available at: https://github.com/qianlima-Iab/MISSL.