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）领域的最先进范式。为提升有限标注数据下的表示质量，对比学习近期在推荐系统中受到关注，并成功增强了基于图的协同过滤模型。然而，大多数对比方法的效果高度依赖于人工构建有效的对比视图以进行启发式数据增强，这难以在不同数据集和下游推荐任务间泛化，且难以自适应数据增强并对噪声扰动保持鲁棒。为填补这一关键空白，本文提出统一的自适应协同过滤（AutoCF）框架，以实现推荐系统的自动数据增强。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，从而自动提取重要的自监督信号。为增强表示判别能力，我们设计了一种掩码图自编码器，在增强过程中通过重构被掩码的子图结构来聚合全局信息。在多个公开数据集上针对商品、场所及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显著优势。我们已在 https://github.com/HKUDS/AutoCF 公开模型实现代码。