XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
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Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The fundamental idea of CL-based recommendation models is to maximize the consistency between representations learned from different graph augmentations of the user-item bipartite graph. In such a self-supervised manner, CL-based recommendation models are expected to extract general features from the raw data to tackle the data sparsity issue. Despite the effectiveness of this paradigm, we still have no clue what underlies the performance gains. In this paper, we first reveal that CL enhances recommendation through endowing the model with the ability to learn more evenly distributed user/item representations, which can implicitly alleviate the pervasive popularity bias and promote long-tail items. Meanwhile, we find that the graph augmentations, which were considered a necessity in prior studies, are relatively unreliable and less significant in CL-based recommendation. On top of 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 create views for CL. A comprehensive experimental study on three 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 is released at https://github.com/Coder-Yu/SELFRec.
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