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（GNNs）在建模协同过滤（CF）中的高阶关系方面具有普遍优势。沿此研究方向，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。尽管许多方法已体现出有效性，但仍有两大关键问题尚未探索：i) 现有大多数基于GCL的CF模型仍受限于忽略用户-物品交互行为常由多样潜在意图因子（如为家庭聚会购物、偏好颜色或品牌）驱动的事实；ii) 其引入的非自适应增强技术易受噪声信息影响，引发对模型鲁棒性及引入误导性自监督信号风险的担忧。针对这些局限，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。通过学习具有全局上下文的解耦表示，DCCF不仅能从纠缠的自监督信号中蒸馏出更细粒度的潜在因子，还能减轻增强引入的噪声。最后，引入跨视图对比学习任务，通过参数化的交互掩码生成器实现自适应增强。在多种公开数据集上的实验表明，我们的方法相较于现有解决方案具有优越性。模型实现已发布于链接 https://github.com/HKUDS/DCCF。