多通道超图对比学习用于矩阵补全
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion
AI总结：
针对矩阵补全中数据稀疏和高阶关系捕捉难题，提出MHCL框架，利用自适应超图学习与多通道跨评分对比学习，在五个数据集上显著优于现有方法。
摘要：
评分是用户显式反馈的典型形式，直观地反映了用户对相关物品的喜爱程度。（评分）矩阵补全本质上是一个评分预测过程，也是推荐系统中的一个重要问题。近年来，图神经网络（GNNs）已被广泛应用于矩阵补全，通过将评分矩阵构建为二部图来捕捉用户对物品的偏好。然而，现有方法在现实场景中因数据稀疏和长尾分布而容易受到影响。此外，GNN的消息传递机制难以捕捉节点之间的高阶相关性和约束，而这些在推荐任务中本质上是很有用的。为了解决这些挑战，我们提出了一种用于矩阵补全的多通道超图对比学习框架，命名为MHCL。具体来说，MHCL自适应地学习超图结构以捕捉节点间的高阶相关性，并通过基于注意力的跨视图聚合联合捕捉局部和全局的协作关系。此外，为了考虑评分的量级和顺序信息，我们将不同的评分子图视为不同的通道，鼓励相邻评分之间的对齐，并通过多通道跨评分对比学习进一步实现不同评分之间的相互增强。在五个公开数据集上的大量实验表明，所提出的方法显著优于当前最先进的方法。
英文摘要：
Rating is a typical user explicit feedback that visually reflects how much a user likes a related item. The (rating) matrix completion is essentially a rating prediction process, which is also a significant problem in recommender systems. Recently, graph neural networks (GNNs) have been widely used in matrix completion, which captures users' preferences over items by formulating a rating matrix as a bipartite graph. However, existing methods are susceptible due to data sparsity and long-tail distribution in real-world scenarios. Moreover, the messaging mechanism of GNNs makes it difficult to capture high-order correlations and constraints between nodes, which are essentially useful in recommendation tasks. To tackle these challenges, we propose a Multi-Channel Hypergraph Contrastive Learning framework for matrix completion, named MHCL. Specifically, MHCL adaptively learns hypergraph structures to capture high-order correlations between nodes and jointly captures local and global collaborative relationships through attention-based cross-view aggregation. Additionally, to consider the magnitude and order information of ratings, we treat different rating subgraphs as different channels, encourage alignment between adjacent ratings, and further achieve the mutual enhancement between different ratings through multi-channel cross-rating contrastive learning. Extensive experiments on five public datasets demonstrate that the proposed method significantly outperforms the current state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
