Biclique-Guided Graph Contrastive Learning for Recommendation

Graph Contrastive Learning (GCL) has been widely adopted in recommendation systems to learn user and item representations from implicit user–item interactions. However, standard GCL formulations define positive pairs as the same node across augmented views and treat all other nodes as negatives, leading to critical limitations in user–item interaction graphs. Specifically, such designs introduce false negatives by contrasting users or items with highly similar interaction patterns and lack explicit semantic guidance, since contrastive pairs are determined implicitly by augmentation mechanics rather than meaningful interaction structures. To address these issues, we propose a biclique-guided graph contrastive learning framework that explicitly leverages user–item interaction structures to guide positive and negative sample selection. We model groups of users and items as soft bicliques, which act as relaxed structural prototypes capturing consistent collective preference patterns. By relaxing the strict connectivity constraints of hard bicliques, our formulation avoids NP-hard exact biclique discovery and remains effective under sparse interactions and missing edges. Nodes within the same soft biclique form positive contrastive pairs, while nodes across different bicliques provide more reliable negatives, injecting explicit semantic supervision beyond instance-level or clustering-based heuristics. Extensive experiments on multiple benchmark datasets demonstrate consistent performance improvements and superior representation quality over state-of-the-art contrastive recommendation methods.