Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, even when labeled data is limited. However, directly applying existing GCL models to real-world recommendation environments poses challenges. There are two primary issues to address. Firstly, the lack of consideration for data noise in contrastive learning can result in noisy self-supervised signals, leading to degraded performance. Secondly, many existing GCL approaches rely on graph neural network (GNN) architectures, which can suffer from over-smoothing problems due to non-adaptive message passing. To address these challenges, we propose a principled framework called GraphAug. This framework introduces a robust data augmentor that generates denoised self-supervised signals, enhancing recommender systems. The GraphAug framework incorporates a graph information bottleneck (GIB)-regularized augmentation paradigm, which automatically distills informative self-supervision information and adaptively adjusts contrastive view generation. Through rigorous experimentation on real-world datasets, we thoroughly assessed the performance of our novel GraphAug model. The outcomes consistently unveil its superiority over existing baseline methods. The source code for our model is publicly available at: https://github.com/HKUDS/GraphAug.

中文翻译：

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用于推荐的图增强

对比学习的图增强由于其即使在标记数据有限的情况下也能够学习富有表现力的用户表示的能力，在推荐系统领域获得了极大的关注。然而，直接将现有的 GCL 模型应用到现实世界的推荐环境中会带来挑战。有两个主要问题需要解决。首先，对比学习中缺乏对数据噪声的考虑可能会导致自监督信号产生噪声，从而导致性能下降。其次，许多现有的 GCL 方法依赖于图神经网络 (GNN) 架构，该架构可能会因非自适应消息传递而遭受过度平滑问题。为了应对这些挑战，我们提出了一个名为 GraphAug 的原则框架。该框架引入了一个强大的数据增强器，可以生成去噪的自我监督信号，从而增强推荐系统。 GraphAug 框架采用了图信息瓶颈（GIB）正则化增强范式，可自动提取信息丰富的自我监督信息并自适应调整对比视图生成。通过对现实世界数据集进行严格的实验，我们彻底评估了新颖的 GraphAug 模型的性能。结果一致揭示了其相对于现有基线方法的优越性。我们模型的源代码可公开获取：https://github.com/HKUDS/GraphAug。