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Multi-view knowledge graph recommendation with dynamic transformer modeling
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AI
DOI：10.1016/j.engappai.2026.114218.png)
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Abstract
En 中文
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 .
Keywords:
Dynamic Tanh Transformer
Multi-view Knowledge Graph
Recommendation Systems
Inter-layer Attention
Data Sparsity
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