祝贺博士一年级吴斌权同学的论文中稿IEEE International Conference on Data Engineering (ICDE 2024) 会议论文。ICDE是数据库研究领域历史悠久的国际会议，属于CCF A类的顶级会议。
论文题目：《When Multi-Behavior Meets Multi-Interest: Multi-Behavior Sequential Recommendation with Multi-Interest Self-Supervised Learning》
论文摘要：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 are 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 further. 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.
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