Knowledge Graph Self-Supervised Rationalization for Recommendation
This work addresses the challenge of effectively leveraging knowledge graphs for recommendation systems, which is an incremental advancement in the field.
The paper tackles the problem of improving knowledge-aware recommender systems by introducing KGRec, a self-supervised rationalization method that uses attentive knowledge rationalization and integrates generative and contrastive tasks, resulting in outperforming state-of-the-art methods on three real-world datasets.
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.