Knowledge-Flow Contrastive Learning for recommendation

Knowledge graphs (KGs) are widely used in recommender systems to alleviate data sparsity and cold-start problems, and improve explainability. Recent advances in graph contrastive learning enable better capture of higher-order structural information and help address the challenge of sparse supervisory signals. However, existing methods often treat contrastive views in isolation using graph neural networks, which can lead to noise accumulation and weaken user preference representations. To address these issues, we propose Knowledge-Flow Contrastive Learning (KFCL), a novel framework that reduces noise by facilitating the propagation of node representations across layers and leverages multi-layer information to guide contrastive learning. We enhance KG representations through heterogeneous relation-aware aggregators and update user and item representations within a collaborative knowledge graph. These representations are further refined through a user–item interaction graph and then undergo feature fusion to better capture user preferences. KFCL also incorporates adaptive layer aggregation and learnable interaction weights to further suppress noise and improve learning stability. Extensive experiments on three public benchmark datasets show that KFCL consistently outperforms state-of-the-art methods.