计算机科学与探索2025，Vol.19Issue(6)：1508-1521,14.DOI:10.3778/j.issn.1673-9418.2407019
融合层级知识图谱嵌入与注意力机制的推荐方法
Hierarchical Knowledge Graph Embedding and Self-Attention Mechanism for Recommendation
摘要
Abstract
Knowledge graphs have been widely applied in recommendation systems,contributing to alleviating the data sparsity issue in user-item interactions.Existing knowledge graph-based recommendation methods primarily rely on path mining or information propagation to explore potential associations between users and items.However,they often fail to fully leverage the rich semantics and structural information within knowledge graphs,and may introduce irrelevant noise,thereby affecting the accuracy of recommendations.To address these problems,this paper proposes a recommendation method that integrates hierarchical knowledge graph embedding and self-attention mechanism(HKSAR),aiming to extract high-order semantics and structural information from knowledge graphs to mitigate the sparsity problem.Specifically,the proposed model first constructs high-order subgraphs for user-item pairs,explicitly depicting the complex relationships between them.Through a hierarchical attention embedding learning process,the model encodes the high-order semantics and topological structure within the subgraphs,and employs a self-attention mechanism to differentiate the importance of each entity in the subgraphs,ultimately generating high-quality subgraph embeddings for accurate user preference modeling.Experimental results on three real-world datasets show that the proposed method achieves an average improvement of 10.7%and 13.6%in Hit and NDCG metrics,respectively,compared with the state-of-the-art baseline model.Moreover,the proposed method consistently outperforms in scenarios with varying degrees of data sparsity,effectively alleviating the data sparsity issue.关键词
推荐系统/图神经网络/知识图谱/自注意力机制/协同过滤Key words
recommendation systems/graph neural networks/knowledge graphs/self-attention mechanism/collaborative filtering分类
计算机与自动化引用本文复制引用
沙潇,王建文,丁建川,徐笑然..融合层级知识图谱嵌入与注意力机制的推荐方法[J].计算机科学与探索,2025,19(6):1508-1521,14.基金项目
河北省教育厅高等学校科学研究项目(QN2024115) （QN2024115）
河北省高校基本科研业务费项目(SYKY2311,SYKY2310). This work was supported by the Science Research Project of Hebei Education Department(QN2024115),and the Fundamental Re-search Funds for Hebei Province(SYKY2311,SYKY2310). （SYKY2311,SYKY2310）
