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<p>To address the issues of weak relational semantic perception, deep-layer information attenuation, and insufficient structural utilization in existing knowledge graph-based recommendation models, this paper proposes a Multi-layer Knowledge Graph-based Recommendation model with Relational Attention (MKGRA).The model consists of three core modules: an entity-relation dual attention mechanism to enhance semantic perception of heterogeneous relations; a multi-layer residual propagation structure to mitigate deep-layer information attenuation; and a graph neural network-based semantic fusion module to improve the utilization of graph structural information. Experiments comparing with 19 baseline models on three datasets (Last-FM, Book-Crossing, and Yelp2018) show that MKGRA significantly outperforms mainstream baseline models on multiple metrics. Its AUC reaches 0.848, 0.754, and 0.881 respectively, while its ACC is 0.771, 0.690, and 0.839.The experimental results verify the effectiveness of each module, providing a more accurate method for semantic modeling and structural mining in knowledge graph-based recommendations.</p>

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Multi-layer knowledge graph recommender with relational attention

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • Hui Wang,
  • Junhao Zhu,
  • Shuxin Yang

摘要

To address the issues of weak relational semantic perception, deep-layer information attenuation, and insufficient structural utilization in existing knowledge graph-based recommendation models, this paper proposes a Multi-layer Knowledge Graph-based Recommendation model with Relational Attention (MKGRA).The model consists of three core modules: an entity-relation dual attention mechanism to enhance semantic perception of heterogeneous relations; a multi-layer residual propagation structure to mitigate deep-layer information attenuation; and a graph neural network-based semantic fusion module to improve the utilization of graph structural information. Experiments comparing with 19 baseline models on three datasets (Last-FM, Book-Crossing, and Yelp2018) show that MKGRA significantly outperforms mainstream baseline models on multiple metrics. Its AUC reaches 0.848, 0.754, and 0.881 respectively, while its ACC is 0.771, 0.690, and 0.839.The experimental results verify the effectiveness of each module, providing a more accurate method for semantic modeling and structural mining in knowledge graph-based recommendations.