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.
翻译:以 GNN 为基础的现有建议系统的关键想法是,在用户-项目互动边缘上反复执行传递的信息,以完善编码嵌入。然而,尽管其有效性,大多数当前建议模型都依赖于充足和高质量的培训数据,因此,学习到的表达方式能够很好地捕捉到用户的准确偏好。在许多实际建议情景中,用户行为数据经常是吵闹的,并显示扭曲的分布,这可能导致基于 GNNN 的模型的亚最佳代表性能。在本文件中,我们建议SHT(SHT),一个全新的自超光速超音速变换器框架(SHT),通过以明确的方式探索全球协作关系来增加用户的表述。具体地说,我们首先授权图表神经化模型模式保持用户和项目之间的全球协作效应,并拥有超光速变异关系网络。在蒸馏的全球背景下,一个交叉视图的自我校准的自我校正化学习组件可能会导致基于GNNN的模型模型模型的超优性代表性。在用户- SHDSL 数据库中,可以大大地展示我们SHDSDSDSL 的模型的模型,从而展示了现有数据分析系统,从而展示了数据-SHIDSM-saldestryal