Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model recently. However, the success of most contrastive methods heavily relies on manually generating effective contrastive views for heuristic-based data augmentation. This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation. To fill this crucial gap, this work proposes a unified Automated Collaborative Filtering (AutoCF) to automatically perform data augmentation for recommendation. Specifically, we focus on the generative self-supervised learning framework with a learnable augmentation paradigm that benefits the automated distillation of important self-supervised signals. To enhance the representation discrimination ability, our masked graph autoencoder is designed to aggregate global information during the augmentation via reconstructing the masked subgraph structures. Experiments and ablation studies are performed on several public datasets for recommending products, venues, and locations. Results demonstrate the superiority of AutoCF against various baseline methods. We release the model implementation at https://github.com/HKUDS/AutoCF.

翻译：图神经网络（GNN）已成为协同过滤（CF）的最先进范式。为提高有限标注数据下的表示质量，对比学习近年来在推荐领域受到关注，并促进了基于图的协同过滤模型的发展。然而，大多数对比方法的成功严重依赖于人工生成有效的对比视图以进行基于启发式的数据增强。这类方法无法在不同数据集和下游推荐任务间泛化，难以自适应地进行数据增强并对噪声扰动保持鲁棒。为弥补这一关键空白，本文提出统一的自动化协同过滤（AutoCF）框架，以实现面向推荐的自动化数据增强。具体而言，我们聚焦于生成式自监督学习框架，采用可学习增强范式，从而有益于重要自监督信号的自动提炼。为增强表示判别能力，我们设计的掩码图自编码器通过重构掩码子图结构，在增强过程中聚合全局信息。我们在多个公开数据集上针对产品、场所和位置推荐进行了实验与消融研究。结果表明AutoCF相较于各类基线方法具有优越性。模型实现已开源至https://github.com/HKUDS/AutoCF。