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.

翻译：图增强结合对比学习因能在标签数据有限的情况下学习富有表现力的用户表征，在推荐系统领域引起了广泛关注。然而，将现有图对比学习模型直接应用于真实推荐环境面临挑战，主要存在两个问题：首先，对比学习中未考虑数据噪声会导致自监督信号含有噪声，进而降低模型性能；其次，许多现有图对比学习方法依赖图神经网络架构，因非自适应消息传递而容易出现过度平滑问题。为解决上述挑战，我们提出名为GraphAug的原创性框架，该框架引入鲁棒的数据增强器以生成去噪的自监督信号，从而增强推荐系统性能。GraphAug框架采用图信息瓶颈正则化的增强范式，能自动提取信息性自监督信号并自适应调整对比视图生成。通过在真实数据集上的严格实验，我们全面评估了新型GraphAug模型的性能，结果一致表明其优于现有基线方法。模型源代码已公开于：https://github.com/HKUDS/GraphAug。