Hypergraph Contrastive Learning with Graph Structure Learning for Recommendation

In this paper, to address the insufficient capturing of high-order correlations and the vulnerability to noise in conventional collaborative filtering models, we propose HyperGraph Contrastive Learning with graph structure learning for recommendation (HGCL). HGCL employs user and item hypergraphs as contrastive views in contrastive learning, where edges connect the item and user's k-order reachable neighbors. This approach allows HGCL to model relationships with distant nodes and explicitly capture high-order correlations, alleviating the issue of over-smoothing. Subsequently, HGCL performs contrastive learning between representations obtained from the user-item interaction graph and hypergraphs. This integrates the user-item relationship features from the interaction graph with the high-order correlations for each user and item from the hypergraphs, resulting in more effective representations. Furthermore, to address the weakness of hypergraphs against noise, we modify the hypergraph structure learning method for the recommendation task and incorporate it into HGCL. Based on the user and item representations, HGCL detects potential node relationships and noise in the hypergraph for the recommendation task. By adding or removing these nodes from the hypergraph, HGCL acquires a denoised hypergraph. By applying these processes to user and item hypergraphs, HGCL obtains improved hypergraphs with reduced noise effects and achieves more effective recommendations. Experimental results on real-world datasets demonstrate that HGCL outperforms baseline models, achieving up to 5.25% improvement in NDCG@20.