Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

We study the sparse supervision problem in knowledge-aware recommender systems and introduce a self-supervised contrastive learning framework. Our approach models multiple graph views at different semantic levels and performs cross-view contrastive learning to enhance representation robustness. Experimental results show consistent gains across multiple public benchmarks.

Recommended citation: Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, and Xin Cao. Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System. SIGIR 2022, 1358-1368.