Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is to recursively perform message passing along user-item interaction edges to refine encoded embeddings, relying on sufficient and high-quality training data. However, user behavior data in practical recommendation scenarios is often noisy and exhibits skewed distribution. To address these issues, some recommendation approaches, such as SGL, leverage self-supervised learning to improve user representations. These approaches conduct self-supervised learning through creating contrastive views, but they depend on the tedious trial-and-error selection of augmentation methods. In this paper, we propose a novel Adaptive Graph Contrastive Learning (AdaGCL) framework that conducts data augmentation with two adaptive contrastive view generators to better empower the CF paradigm. Specifically, we use two trainable view generators - a graph generative model and a graph denoising model - to create adaptive contrastive views. With two adaptive contrastive views, AdaGCL introduces additional high-quality training signals into the CF paradigm, helping to alleviate data sparsity and noise issues. Extensive experiments on three real-world datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods. Our model implementation codes are available at the link https://github.com/HKUDS/AdaGCL.

翻译：图神经网络（GNNs）近期已成为推荐系统中一种有效的协同过滤（CF）方法。基于GNN的推荐系统的核心思想是沿用户-物品交互边递归执行消息传递以优化编码嵌入，这依赖于充足且高质量的训练数据。然而，实际推荐场景中的用户行为数据往往存在噪声且呈现偏态分布。为解决这些问题，SGL等推荐方法利用自监督学习改进用户表示。这些方法通过创建对比视图进行自监督学习，但依赖于繁琐的试错式数据增强方法选择。本文提出一种新型自适应图对比学习（AdaGCL）框架，通过两个自适应对比视图生成器进行数据增强，以更有效地增强CF范式。具体而言，我们使用两个可训练的视图生成器——图生成模型与图去噪模型——来创建自适应对比视图。借助两个自适应对比视图，AdaGCL为CF范式引入了额外的高质量训练信号，有助于缓解数据稀疏与噪声问题。在三个真实数据集上的大量实验表明，我们的模型优于多种现有最优推荐方法。模型实现代码已开源至链接：https://github.com/HKUDS/AdaGCL。