  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.
