Multi-behavior recommendation (MBR) has garnered growing attention recently due to its ability to mitigate the sparsity issue by inferring user preferences from various auxiliary behaviors to improve predictions for the target behavior. Although existing research on MBR has yielded impressive results, they still face two major limitations. First, previous methods mainly focus on modeling fine-grained interaction information between users and items under each behavior, which may suffer from sparsity issue. Second, existing models usually concentrate on exploiting dependencies between two consecutive behaviors, leaving intra- and inter-behavior consistency largely unexplored. To the end, we propose a novel approach named Hypergraph Enhanced Cascading Graph Convolution Network for multi-behavior recommendation (HEC-GCN). To be specific, we first explore both fine- and coarse-grained correlations among users or items of each behavior by simultaneously modeling the behavior-specific interaction graph and its corresponding hypergraph in a cascaded manner. Then, we propose a behavior consistency-guided alignment strategy that ensures consistent representations between the interaction graph and its associated hypergraph for each behavior, while also maintaining representation consistency across different behaviors. Extensive experiments and analyses on three public benchmark datasets demonstrate that our proposed approach is consistently superior to previous state-of-the-art methods due to its capability to effectively attenuate the sparsity issue as well as preserve both intra- and inter-behavior consistencies. The code is available at https://github.com/marqu22/HEC-GCN.git.

翻译：多行为推荐（MBR）近年来受到越来越多的关注，因为它能够通过从各种辅助行为推断用户偏好来缓解稀疏性问题，从而改进目标行为的预测。尽管现有的多行为推荐研究已取得显著成果，但仍面临两大局限。首先，先前方法主要关注建模每种行为下用户与物品之间的细粒度交互信息，这可能受限于稀疏性问题。其次，现有模型通常集中于利用两个连续行为之间的依赖关系，而行为内部和行为间的一致性在很大程度上未被探索。为此，我们提出了一种名为超图增强级联图卷积网络（HEC-GCN）的新方法用于多行为推荐。具体而言，我们首先通过以级联方式同时建模行为特定的交互图及其对应的超图，探索每种行为中用户或物品之间的细粒度和粗粒度关联。然后，我们提出一种行为一致性引导的对齐策略，该策略确保每种行为的交互图与其关联超图之间的表示一致性，同时保持不同行为间的表示一致性。在三个公开基准数据集上的大量实验和分析表明，由于能够有效缓解稀疏性问题并保持行为内部和行为间的一致性，我们提出的方法始终优于先前的最先进方法。代码可在 https://github.com/marqu22/HEC-GCN.git 获取。