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 2–5% AUC improvements across multiple public benchmarks.