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的推荐模型核心原则是确保用户-物品二部图不同图增强方式所得表征之间的一致性。这种自监督方法能从原始数据中提取通用特征，从而缓解数据稀疏问题。尽管该范式效果显著，但其性能提升的成因尚未完全明晰。本文对CL在推荐中的影响提供了新见解。研究发现表明，CL能促使模型学习更均匀分布的用户与物品表征，从而缓解普遍存在的流行度偏差并促进长尾物品推荐。我们的分析还指出，以往被视为关键的图增强方法在基于CL的推荐中可靠性较低且意义有限。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），该方法摒弃了无效的图增强，转而采用简单而有效的噪声增强嵌入来生成CL所需视图。在四个大规模高稀疏基准数据集上的综合实验表明，尽管所提方法极其简单，但能平滑调整所学表征的均匀性，并在推荐准确率和训练效率两方面均显著优于基于图增强的同类方法。代码及所用数据集已开源至https://github.com/Coder-Yu/SELFRec。