This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We highlight the importance of high-quality data augmentation with relevant self-supervised pretext tasks for improving performance. Towards this end, we propose a new approach that automates the self-supervision augmentation process through a rationale-aware generative SSL that distills informative user-item interaction patterns. The proposed recommender with Graph TransFormer (GFormer) that offers parameterized collaborative rationale discovery for selective augmentation while preserving global-aware user-item relationships. In GFormer, we allow the rationale-aware SSL to inspire graph collaborative filtering with task-adaptive invariant rationalization in graph transformer. The experimental results reveal that our GFormer has the capability to consistently improve the performance over baselines on different datasets. Several in-depth experiments further investigate the invariant rationale-aware augmentation from various aspects. The source code for this work is publicly available at: https://github.com/HKUDS/GFormer.

翻译：本文提出了一种新颖的表示学习方法，通过将生成式自监督学习与图变换器架构相结合，应用于推荐系统。我们强调了高质量数据增强及相关的自监督预训练任务对提升性能的重要性。为此，我们提出了一种新方法，该方法通过一种基于原理感知的生成式自监督学习来自动化自监督增强过程，从而提炼出信息丰富的用户-项目交互模式。所提出的推荐系统采用了图变换器（GFormer），它提供了参数化的协作原理发现机制，用于选择性增强，同时保持全局感知的用户-项目关系。在GFormer中，我们允许原理感知的自监督学习启发图协同过滤，在图变换器中实现任务自适应的不变原理合理化。实验结果表明，我们的GFormer能够在不同数据集上持续提升相对于基线的性能。多项深入实验进一步从多个方面探究了这种不变原理感知增强方法。本工作的源代码已公开在：https://github.com/HKUDS/GFormer。