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:合成神经网络(GNNS)已成为合作过滤(CF)的最先进范例。为了提高有限标签数据的代表性质量,对比式学习在建议中引起了关注,最近也使基于图形的CF模型受益。然而,大多数对比性方法的成功在很大程度上依赖于人工生成有效的对比性观点,以扩大基于外观的数据。这并没有在不同的数据集和下游建议任务中一概而论,这很难适应数据增强和对噪音扰动的强力。为了填补这一关键空白,这项工作提议建立一个统一的自动协作过滤(AutoCF)系统,以自动执行建议的数据增强。具体地说,我们侧重于基因化自监督式学习框架,采用可学习的增强模式,有利于对基于外观的重要信号进行自动蒸馏。为了增强代表能力,我们的遮盖式图形自动解析器自动解析器设计在增强过程中通过重建遮蔽子图结构来汇总全球信息。在建议产品、地点和地点上自动透析(WeCFA/ODF)的多个公共数据集进行实验和升级研究。</s>