Graph Transformer for Recommendation

This paper presents a novel approach to representation learning in recommender systems by integrating <PRE_TAG>generative self-supervised learning</POST_TAG> with <PRE_TAG>graph transformer architecture</POST_TAG>. 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 <PRE_TAG>rationale-aware generative SSL</POST_TAG> 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 <PRE_TAG>global-aware user-item relationships</POST_TAG>. 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.