Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation
In recent years, the use of edge information provided by knowledge graphs together with the advantages of higher-order connectivity in graph neural networks for recommendation systems has become an important research direction. However, existing approaches are often limited by sparse labels, insufficient graph structure learning, and noisy entities in the knowledge graph, which reduce recommendation accuracy. To address these limitations, we propose a multi-view graph contrastive learning framework. The proposed method enhances user representations through multi-view knowledge graph distillation, enabling more accurate modeling of user preferences over entities and relations. The network ...
| Authors | Zhifei Hu, Feng Xia |
|---|---|
| Primary category | cs.IR |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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