  Graph Neural Networks (GNNs) have been shown as promising solutions for
collaborative filtering (CF) with the modeling of user-item interaction graphs.
The key idea of existing GNN-based recommender systems is to recursively
perform the message passing along the user-item interaction edge for refining
the encoded embeddings. Despite their effectiveness, however, most of the
current recommendation models rely on sufficient and high-quality training
data, such that the learned representations can well capture accurate user
preference. User behavior data in many practical recommendation scenarios is
often noisy and exhibits skewed distribution, which may result in suboptimal
representation performance in GNN-based models. In this paper, we propose SHT,
a novel Self-Supervised Hypergraph Transformer framework (SHT) which augments
user representations by exploring the global collaborative relationships in an
explicit way. Specifically, we first empower the graph neural CF paradigm to
maintain global collaborative effects among users and items with a hypergraph
transformer network. With the distilled global context, a cross-view generative
self-supervised learning component is proposed for data augmentation over the
user-item interaction graph, so as to enhance the robustness of recommender
systems. Extensive experiments demonstrate that SHT can significantly improve
the performance over various state-of-the-art baselines. Further ablation
studies show the superior representation ability of our SHT recommendation
framework in alleviating the data sparsity and noise issues. The source code
and evaluation datasets are available at: https://github.com/akaxlh/SHT.
