Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item representations. While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact that user-item interaction behaviors are often driven by diverse latent intent factors (e.g., shopping for family party, preferred color or brand of products); ii) Their introduced non-adaptive augmentation techniques are vulnerable to noisy information, which raises concerns about the model's robustness and the risk of incorporating misleading self-supervised signals. In light of these limitations, we propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation in an adaptive fashion. With the learned disentangled representations with global context, our DCCF is able to not only distill finer-grained latent factors from the entangled self-supervision signals but also alleviate the augmentation-induced noise. Finally, the cross-view contrastive learning task is introduced to enable adaptive augmentation with our parameterized interaction mask generator. Experiments on various public datasets demonstrate the superiority of our method compared to existing solutions. Our model implementation is released at the link https://github.com/HKUDS/DCCF.

翻译：近期研究表明，图神经网络（GNN）在建模协同过滤（CF）中的高阶关系方面具有显著优势。在此基础上，图对比学习（GCL）通过学习增强的用户和物品表示，在解决监督标签稀缺问题上展现出强大性能。尽管许多方法已取得有效成果，但仍有两个关键问题尚未探索：i) 现有基于GCL的CF模型大多局限于忽略用户-物品交互行为常由多样潜在意图因素驱动（例如，为家庭聚会购物、偏好产品颜色或品牌）的事实；ii) 其引入的非自适应增强技术易受噪声信息影响，引发模型鲁棒性及引入误导性自监督信号风险的担忧。针对上述局限，我们提出解耦对比协同过滤框架（DCCF），以自适应方式通过自监督增强实现意图解耦。借助学习到的全局上下文解耦表示，我们的DCCF不仅能从纠缠的自监督信号中提炼更细粒度的潜在因素，还能缓解增强引入的噪声。最后，引入跨视图对比学习任务，通过参数化交互掩码生成器实现自适应增强。在多个公开数据集上的实验表明，本方法相较于现有解决方案具有优越性。我们的模型实现已发布于链接https://github.com/HKUDS/DCCF。