Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling

TL;DR

This paper introduces a novel graph collaborative filtering method that models user intents and item properties through co-clustering and contrastive learning, improving recommendation accuracy by capturing complex user-item relationships.

Contribution

It proposes uniformly co-clustered intent modeling with contrastive and co-clustering modules, capturing nuanced user-item relations and intent compatibility, which previous methods overlooked.

Findings

Improved recommendation performance on real-world datasets.

Effective modeling of user intents and item properties.

Theoretical validation of the mutual information maximization.

Abstract

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.