Knowledge Graph Self-Supervised Rationalization for Recommendation

Abstract

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.

arXiv commentAccepted by KDD'23

References in corpus (24)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Neural Graph Collaborative Filtering
• KGAT: Knowledge Graph Attention Network for Recommendation
• Self-supervised Graph Learning for Recommendation
• Graph Convolutional Matrix Completion
• Knowledge Graph Convolutional Networks for Recommender Systems
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Knowledge Graph Contrastive Learning for Recommendation
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
• Masked Autoencoders As Spatiotemporal Learners
• Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
• Multi-Modal Self-Supervised Learning for Recommendation
• DKN: Deep Knowledge-Aware Network for News Recommendation
• Debiased Contrastive Learning for Sequential Recommendation
• Automated Self-Supervised Learning for Recommendation
• TransA: An Adaptive Approach for Knowledge Graph Embedding
• Graph Transformer for Recommendation
• Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
• Graph-less Collaborative Filtering
• Stochastic Client Selection for Federated Learning with Volatile Clients
• Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

Cited by in corpus (8)

• A Comprehensive Survey on Deep Graph Representation Learning
• SSLRec: A Self-Supervised Learning Framework for Recommendation
• A Comprehensive Survey on Self-Supervised Learning for Recommendation
• Comprehending Knowledge Graphs with Large Language Models for Recommender Systems
• RecKG: Knowledge Graph for Recommender Systems
• LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
• KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?
• Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph