In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge connections, we propose an attentive knowledge rationalization mechanism that generates rational scores for knowledge triplets. With these scores, KGRec integrates generative and contrastive self-supervised tasks for recommendation through rational masking. To highlight rationales in the knowledge graph, we design a novel generative task in the form of masking-reconstructing. By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales. To further rationalize the effect of collaborative interactions on knowledge graph learning, we introduce a contrastive learning task that aligns signals from knowledge and user-item interaction views. To ensure noise-resistant contrasting, potential noisy edges in both graphs judged by the rational scores are masked. Extensive experiments on three real-world datasets demonstrate that KGRec outperforms state-of-the-art methods. We also provide the implementation codes for our approach at https://github.com/HKUDS/KGRec.

翻译：本文提出一种名为KGRec的新型自监督理性化方法，用于知识感知推荐系统。为有效识别信息性知识连接，我们提出一种注意力知识理性化机制，为知识三元组生成理性化分数。基于这些分数，KGRec通过理性化掩码整合生成式与对比式自监督任务以实现推荐。为突出知识图谱中的理性解释，我们设计了一种掩码-重建形式的生成式任务：通过掩码具有高理性化分数的重要知识，训练KGRec重建并突出作为理性解释的有用知识连接。为进一步理性化协同交互对知识图谱学习的影响，我们引入对比学习任务，对齐来自知识视图与用户-物品交互视图的信号。为确保抗噪对比效果，根据理性化分数判断两图中潜在噪声边并加以掩码。在三个真实世界数据集上的大量实验表明，KGRec优于现有最先进方法。我们同时提供方法实现代码，详见https://github.com/HKUDS/KGRec。