Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-the-art performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, so as to comprehensively capture the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods, and the robustness against sparse user interaction data. Our model implementation codes are available at https://github.com/akaxlh/HCCF.

翻译：协作过滤(CF)是将用户和项目纳入潜在代表空间的参数化的基本范例,其相关模式来自互动数据; 在各种CF技术中,开发基于GNN的推荐系统,例如PinSage和LightGCN, 提供了最先进的绩效;然而,现有解决方案中尚未充分探讨两项关键挑战:(一) 以更深的图形为基础的CF结构造成的过度吸附效应,可能导致无法区分的用户的稳健表现和建议结果的退化。 (二) 监督信号(即用户项目互动)通常稀缺,在现实中分布偏斜,限制了基于GNNNE的推荐系统的代表性。 为了应对这些挑战,我们提出了一个新的自我监督的建议框架,即超光速对比协作过滤(HCC),以联合捕捉与高压模型强化的交叉对比学习结构的本地和全球合作关系。 设计高光学结构加强了基于GNNCF的模型化用户互动能力, 从而将我们现有系统内部数据质量代表系统与基于高清晰的高级数据系统整合。