现代情报2025，Vol.45Issue(4)：12-22,11.DOI:10.3969/j.issn.1008-0821.2025.04.002
融合社交关系和知识图谱的双图注意力推荐模型
Dual-Graph Attention Network Recommendation Algorithm Combining Social Relationship and Knowledge Graph
摘要
Abstract
[Purpose/Significance]Current mainstream recommendation algorithms based on knowledge graph mainly focus on mining and utilizing item-side knowledge,paying less attention to user-side auxiliary information,leading to is-sues such as sparse user data and insufficient mining depth.[Method/Process]Addressing the structural and feature differences between user-side and item-side auxiliary information,the study proposed a dual-graph attention recommenda-tion model that integrated social relationships and knowledge graphs.Firstly,the user social network graph and item knowl-edge graph were separately fused with the user-item interaction graph to obtain the user social relationship collaborative graph and item collaborative graph.Secondly,the study used a dual-graph attention network to process these two knowl-edge graphs separately,extracting different user and item feature vectors.Then,through the attention mechanism,the ex-tracted user and item feature vectors were merged.Finally,the interaction probability of users and items was calculated u-sing inner product operation for recommendation.[Result/Conclusion]The study conductes experiments on the Last-FM and Douban datasets,demonstrating that the model outperforms other baseline models on various datasets.关键词
推荐系统/知识图谱/社交关系/注意力机制/图注意力网络Key words
recommended system/knowledge graph/social relations/attention mechanism/graph attention net-work分类
社会科学引用本文复制引用
张彬,祖后敏,吴姣..融合社交关系和知识图谱的双图注意力推荐模型[J].现代情报,2025,45(4):12-22,11.基金项目
河北省社会科学基金项目"新媒体融合时代高校文科学报数据资源整合与影响力提升研究"(项目编号:HB22TQ004). （项目编号:HB22TQ004）
