  By treating users' interactions as a user-item graph, graph learning models
have been widely deployed in Collaborative Filtering(CF) based recommendation.
Recently, researchers have introduced Graph Contrastive Learning(GCL)
techniques into CF to alleviate the sparse supervision issue, which first
constructs contrastive views by data augmentations and then provides
self-supervised signals by maximizing the mutual information between
contrastive views. Despite the effectiveness, we argue that current GCL-based
recommendation models are still limited as current data augmentation
techniques, either structure augmentation or feature augmentation. First,
structure augmentation randomly dropout nodes or edges, which is easy to
destroy the intrinsic nature of the user-item graph. Second, feature
augmentation imposes the same scale noise augmentation on each node, which
neglects the unique characteristics of nodes on the graph. To tackle the above
limitations, we propose a novel Variational Graph Generative-Contrastive
Learning(VGCL) framework for recommendation. Specifically, we leverage
variational graph reconstruction to estimate a Gaussian distribution of each
node, then generate multiple contrastive views through multiple samplings from
the estimated distributions, which builds a bridge between generative and
contrastive learning. Besides, the estimated variances are tailored to each
node, which regulates the scale of contrastive loss for each node on
optimization. Considering the similarity of the estimated distributions, we
propose a cluster-aware twofold contrastive learning, a node-level to encourage
consistency of a node's contrastive views and a cluster-level to encourage
consistency of nodes in a cluster. Finally, extensive experimental results on
three public datasets clearly demonstrate the effectiveness of the proposed
model.
