Graph-based collaborative filtering has emerged as a powerful paradigm for delivering personalized recommendations. Despite their demonstrated effectiveness, these methods often neglect the underlying intents of users, which constitute a pivotal facet of comprehensive user interests. Consequently, a series of approaches have arisen to tackle this limitation by introducing independent intent representations. However, these approaches fail to capture the intricate relationships between intents of different users and the compatibility between user intents and item properties. To remedy the above issues, we propose a novel method, named uniformly co-clustered intent modeling. Specifically, we devise a uniformly contrastive intent modeling module to bring together the embeddings of users with similar intents and items with similar properties. This module aims to model the nuanced relations between intents of different users and properties of different items, especially those unreachable to each other on the user-item graph. To model the compatibility between user intents and item properties, we design the user-item co-clustering module, maximizing the mutual information of co-clusters of users and items. This approach is substantiated through theoretical validation, establishing its efficacy in modeling compatibility to enhance the mutual information between user and item representations. Comprehensive experiments on various real-world datasets verify the effectiveness of the proposed framework.

翻译：基于图的协同过滤已成为提供个性化推荐的强大范式。尽管这些方法展现了显著效果，但往往忽略了用户的潜在意图，而意图构成了用户全面兴趣的关键方面。为此，一系列方法通过引入独立意图表示来应对这一局限。然而，这些方法未能捕捉不同用户意图之间的复杂关系，以及用户意图与物品属性之间的兼容性。为解决上述问题，我们提出了一种名为均匀共聚类意图建模的新方法。具体而言，我们设计了一个均匀对比式意图建模模块，将具有相似意图的用户嵌入和具有相似属性的物品嵌入聚合在一起。该模块旨在对不同用户的意图与不同物品属性之间的细微关系进行建模，尤其是那些在用户-物品图上彼此不可达的关系。为建模用户意图与物品属性之间的兼容性，我们设计了用户-物品共聚类模块，最大化用户与物品共聚类的互信息。该方法通过理论验证得到了证实，确立了其在建模兼容性以增强用户与物品表示互信息方面的有效性。在多个真实世界数据集上的综合实验验证了所提框架的有效性。