Multi-view knowledge graph recommendation with dynamic transformer modeling

Recommender systems are essential for alleviating information overload but continue to face challenges such as data sparsity, cold-start issues, and noisy knowledge graph structures. To address these problems, we propose a novel recommendation framework that integrates a Dynamic Tanh Transformer (DyT-Transformer), a multi-view knowledge graph propagation strategy, and an inter-layer attention mechanism. The DyT-Transformer enhances user-item representations by dynamically weighting key features without normalization layers, improving stability and efficiency. Multi-view propagation is employed to generate diverse graph views through randomized perturbations, which mitigates noise and improves robustness. Inter-layer attention adaptively aggregates embedding across propagation depths to capture hierarchical semantics. Extensive experiments on five real-world datasets, including Book-Crossing, MovieLens-1M, Amazon-Book, Yelp2018, and Alibaba, demonstrate that the proposed model consistently outperforms state-of-the-art baselines in both click-through rate prediction and Top-K recommendation tasks. These results confirm the effectiveness and scalability of combining DyT-Transformer with multi-view knowledge graph learning for robust recommendation. We have made the source code of our proposed model available at https: //github. com/tttterter/MVLDT.

Authors

• Mengting Zhang
• Man Li The Hong Kong University of Science and Technology
• Wenming Cao College of Information Engineering, Shenzhen University, China
• Mingming Yang
• Bing Li
• Guangzhen Zhu
• Lu Cheng University of Illinois at Chicago

Keywords

• Knowledge graph recommendation systems
• Multi-view learning
• Inter-layer attention
• Dynamic Tanh Transformer