Simplified Graph Contrastive Learning for Recommendation with Direct Optimization of Alignment and Uniformity

Graph contrastive learning has been widely used in recommender systems to extract meaningful representations by analyzing the similarities and differences between data samples. However, existing methods often suffer from complex architectures, inefficient representation learning, and lack of attention to the essential properties required for effective embedding. To address these issues, we propose the simplified graph contrastive learning for recommendation with direct optimization of alignment and uniformity (SGCL) method. Our method first constructs a single contrast learning view and directly optimizes two key properties: alignment (to ensure that positive user-item pairs are tightly localized in the embedding space) and uniformity (to maintain a uniform distribution of embeddings across the vector space). Second, controlled noise is also introduced into the embedding space to further refine the distribution of the learned representations. This improves the quality of user and project embeddings while reducing computational complexity. Finally, the main recommendation task is jointly trained with the contrastive learning task. Extensive experiments on the Yelp2018, Alibaba-iFashion, and Amazon-book datasets show that SGCL outperforms the baseline model, LightGCN, with 30% and 36% improvement in Recall@20 and NDCG@20, respectively. These results are especially significant in sparse data scenarios, where the model exhibits excellent performance.