计算机工程与应用2025，Vol.61Issue(19)：158-166,9.DOI:10.3778/j.issn.1002-8331.2407-0354
融合知识图谱与高阶信息聚合机制的推荐模型
Recommendation Model Integrating Knowledge Graph and High-Order Information Aggregation Mechanism
摘要
Abstract
The knowledge graph significantly enhances the accuracy and interpretability of recommendation models through its rich semantic information and complex relational network.Existing knowledge graph-based recommendation models that utilize embedding propagation face challenges in balancing the capture of multi-level semantic associations and the suppression of noise introduced by multi-hop propagation during the aggregation of high-order information.Addi-tionally,during the information propagation process,high-degree nodes tend to dominate feature updates,which dilutes the personalized features of low-degree nodes,thereby weakening the model's ability to capture fine-grained personali-zation.To address these issues,this paper proposes a recommendation model that integrates knowledge graph and high-order information aggregation mechanisms.Firstly,the model uses the neighborhood information of entities as the percep-tual domain and,through multiple iterations of propagation,effectively captures the high-order connectivity and complex relations within the knowledge graph.Secondly,a symmetric normalization mechanism is introduced to address the fea-ture update bias caused by degree distribution imbalance during node aggregation,ensuring balanced representations of different entities in the embedding space.Finally,a high-order aggregation propagation mechanism is designed to dynami-cally integrate multi-level neighborhood features,balancing the capture of high-order semantic information with noise suppression introduced by multi-hop propagation.Comparative experiments with baseline models on the Last-FM and Book-Crossing public datasets demonstrate that the model outperforms other advanced models in AUC,F1,Recall@k and NDCG@k indicators.关键词
推荐模型/知识图谱/图卷积神经网络Key words
recommendation model/knowledge graph/graph convolutional neural network分类
信息技术与安全科学引用本文复制引用
武杲昊,王霞,郝国生,祝义..融合知识图谱与高阶信息聚合机制的推荐模型[J].计算机工程与应用,2025,61(19):158-166,9.基金项目
国家自然科学基金(62277030,62077029) （62277030,62077029）
江苏师范大学2024年研究生科研与实践创新计划项目(2024XKT2627). （2024XKT2627）
