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.

翻译：通过将用户交互视为用户-物品图，图学习模型已被广泛部署于基于协同过滤的推荐系统中。近期，研究者将图对比学习技术引入协同过滤以缓解稀疏监督问题，该技术首先通过数据增强构建对比视图，然后通过最大化对比视图间的互信息提供自监督信号。尽管效果显著，我们认为当前基于图对比学习的推荐模型仍受限于现有数据增强技术（结构增强或特征增强）。首先，结构增强随机丢弃节点或边，易破坏用户-物品图的内在本质。其次，特征增强对每个节点施加相同尺度的噪声增强，忽视了图中节点的独有特性。为解决上述局限，我们提出一种新颖的变分图生成-对比学习框架用于推荐。具体而言，我们利用变分图重建估计每个节点的高斯分布，通过从估计分布中多次采样生成多个对比视图，从而在生成式学习与对比学习之间建立桥梁。此外，估计的方差针对每个节点定制，在优化过程中调节各节点对比损失的尺度。考虑到估计分布的相似性，我们提出一种聚类感知的双重对比学习：节点级别鼓励节点自身对比视图的一致性，聚类级别鼓励聚类内节点的一致性。最后，在三个公开数据集上的广泛实验结果清晰证明了所提模型的有效性。