  Graph Convolutional Networks (GCNs) has demonstrated promising results for
recommender systems, as they can effectively leverage high-order relationship.
However, these methods usually encounter data sparsity issue in real-world
scenarios. To address this issue, GCN-based recommendation methods employ
contrastive learning to introduce self-supervised signals. Despite their
effectiveness, these methods lack consideration of the significant degree
disparity between head and tail nodes. This can lead to non-uniform
representation distribution, which is a crucial factor for the performance of
contrastive learning methods. To tackle the above issue, we propose a novel
Long-tail Augmented Graph Contrastive Learning (LAGCL) method for
recommendation. Specifically, we introduce a learnable long-tail augmentation
approach to enhance tail nodes by supplementing predicted neighbor information,
and generate contrastive views based on the resulting augmented graph. To make
the data augmentation schema learnable, we design an auto drop module to
generate pseudo-tail nodes from head nodes and a knowledge transfer module to
reconstruct the head nodes from pseudo-tail nodes. Additionally, we employ
generative adversarial networks to ensure that the distribution of the
generated tail/head nodes matches that of the original tail/head nodes.
Extensive experiments conducted on three benchmark datasets demonstrate the
significant improvement in performance of our model over the state-of-the-arts.
Further analyses demonstrate the uniformity of learned representations and the
superiority of LAGCL on long-tail performance. Code is publicly available at
https://github.com/im0qianqian/LAGCL
