  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.
