基于超图的协同信号去噪与结构增强方法

Hypergraph-Based Collaborative Signal Denoising and Structural Enhancement Method

摘要: 随着图神经网络的发展，基于图结构的协同过滤方法在推荐系统中得到了广泛应用。这类方法通常将用户和项目表示为二分图中的节点，并通过多层图传播来学习用户与项目之间的高阶协同信息，在推荐性能上取得了较好的效果。然而，在实际推荐场景中，现有方法大多仅依赖用户–项目二分图邻接矩阵进行建模，仍然受到数据稀疏性和结构噪声的影响。一方面，用户行为数据通常呈现长尾分布，活跃用户的频繁交互容易带来冗余甚至噪声信息，而交互较少的用户和项目由于邻接信息不足，其表示难以通过图传播得到有效提升。另一方面，传统二分图只刻画了用户与项目之间的显式交互关系，未能充分利用用户之间以及项目之间潜在的关联信息，限制了模型对协同信号的表达能力。针对上述问题，本文引入超图建模思想，从结构层面对原有图进行改进，提出一种基于超图的协同信号去噪与结构增强方法(HSAAF)。该方法采用预训练–增强的两阶段学习框架：首先在预训练阶段从原始交互数据中学习较为稳定的用户和项目表示，随后在增强阶段构建用户超图和项目超图，用以刻画用户–用户和项目–项目之间的高阶关联，并将其融合到原有邻接结构中，得到结构更加完整、噪声更可控的增强图。通过对图结构的整体优化，HSAAF能够在一定程度上减少活跃用户带来的噪声影响，同时提升稀疏用户和长尾项目的表示质量，从而增强模型在复杂推荐场景下的稳定性和泛化能力。

Abstract: With the development of graph neural networks, graph-based collaborative filtering methods have been widely applied in recommender systems. These methods usually model users and items as nodes in a bipartite graph and learn high-order collaborative information through multi-layer graph propagation, achieving promising performance in recommendation tasks. However, in real-world scenarios, most existing approaches rely solely on the user-item bipartite adjacency matrix, and thus are still affected by data sparsity and structural noise. On the one hand, user interaction data often follow a long-tail distribution, where frequent interactions from active users may introduce redundant or noisy information, while users and items with few interactions are difficult to be effectively enhanced due to limited neighborhood information. On the other hand, traditional bipartite graphs only model explicit user-item interactions and fail to fully exploit potential relationships among users or among items. To address these issues, this paper introduces hypergraph modeling to improve the graph structure and proposes a hypergraph-based collaborative signal denoising and structural enhancement method (HSAAF). The proposed method adopts a two-stage pretraining-augmentation framework. In the pretraining stage, stable user and item representations are learned from the original interaction data. In the augmentation stage, user and item hypergraphs are constructed to model high-order relationships among users and among items, which are then integrated into the original graph structure to obtain an enhanced graph with more complete structural information and reduced noise. By optimizing the graph structure as a whole, HSAAF can alleviate the negative impact of noisy interactions from active users and improve the representation quality of sparse users and long-tail items, thereby enhancing the stability and generalization ability of the recommendation model in complex scenarios.