Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The underlying principle of CL-based recommendation models is to ensure the consistency between representations derived from different graph augmentations of the user-item bipartite graph. This self-supervised approach allows for the extraction of general features from raw data, thereby mitigating the issue of data sparsity. Despite the effectiveness of this paradigm, the factors contributing to its performance gains have yet to be fully understood. This paper provides novel insights into the impact of CL on recommendation. Our findings indicate that CL enables the model to learn more evenly distributed user and item representations, which alleviates the prevalent popularity bias and promoting long-tail items. Our analysis also suggests that the graph augmentations, previously considered essential, are relatively unreliable and of limited significance in CL-based recommendation. Based on these findings, we put forward an eXtremely Simple Graph Contrastive Learning method (XSimGCL) for recommendation, which discards the ineffective graph augmentations and instead employs a simple yet effective noise-based embedding augmentation to generate views for CL. A comprehensive experimental study on four large and highly sparse benchmark datasets demonstrates that, though the proposed method is extremely simple, it can smoothly adjust the uniformity of learned representations and outperforms its graph augmentation-based counterparts by a large margin in both recommendation accuracy and training efficiency. The code and used datasets are released at https://github.com/Coder-Yu/SELFRec.

翻译：对比学习（CL）近期被证明对提升推荐性能至关重要。基于CL的推荐模型的核心原则是确保用户-物品二分图不同图增强所得表示之间的一致性。这种自监督方法能够从原始数据中提取通用特征，从而缓解数据稀疏性问题。尽管该范式效果显著，但其性能提升的成因尚未完全明晰。本文为CL对推荐的影响提供了新颖见解。我们的发现表明，CL使模型能够学习到分布更均匀的用户和物品表示，从而缓解了普遍的流行度偏差并促进长尾物品的推荐。我们的分析还指出，此前被认为至关重要的图增强方法在基于CL的推荐中相对不可靠且意义有限。基于这些发现，我们提出了一种面向推荐的极简图对比学习方法（XSimGCL），该方法摒弃了低效的图增强，转而采用简单但有效的基于噪声的嵌入增强来生成对比视图。在四个大规模高稀疏基准数据集上的综合实验研究表明，尽管所提方法极其简单，但能够平滑调整所学表示的均匀性，并在推荐准确性和训练效率上大幅优于基于图增强的对比方法。相关代码及数据集已发布于https://github.com/Coder-Yu/SELFRec。