XSGCL: A Lightweight Graph Contrastive Learning Framework for Recommendation

Traditional recommendation models based on contrastive learning first perform data augmentation on the original interaction graph and then strive to improve the consistency of representations encoded from different views. Although this method has been proven effective, recent research has found that graph augmentation often introduces bias owing to the power-law distribution of node edges in graph data: such biases are detrimental to contrastive learning. In addition, the graph structure distribution makes the processing of large-scale datasets computationally intensive, limiting the flexibility of contrastive learning models. To address these challenges, this study proposes a High-Low Variance Separation feature enhancement method (HLVS), which not only avoids direct perturbations to the graph structure but also alleviates the semantic bias problem that exists in traditional feature perturbation methods. Simultaneously, to alleviate the issue of popularity bias in recommendation systems, popularity metrics are introduced into the main task, and a new loss function, Popularity Bayesian Personalized Ranking (PBPR) loss, is designed to balance the representation of popular and unpopular nodes. Finally, by integrating contrastive learning, HLVS, and PBPR, a lightweight and parameter-free graph contrastive learning framework, eXtremely Simple Graph Contrastive Learning (XSGCL), is designed, which can be naturally integrated into recommendation models to improve training efficiency and performance. Extensive experiments on five public datasets prove that integrating XSGCL into LightGCN not only significantly improves training efficiency but also achieves a performance that is better or comparable to that of advanced models. For example, on the Yelp2018 dataset, compared to LightGCN, the proposed model improves training efficiency by 91.2%. On the Alibaba-iFashion dataset, Recall@10 and NDCG@10 indicators increase by 32.21% and 33.73%, respectively.