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

作　　者：张彬 祖后敏 吴姣 Zhang Bin;Zu Houmin;Wu Jiao(School of Cybersecurity and Computer Science,Hebei University,Baoding 071000,China;Periodical Press,Hebei University,Baoding 071000,China)

机构地区：[1]河北大学网络空间安全与计算机学院,河北保定071000 [2]河北大学期刊社,河北保定071000

出　　处：《现代情报》2025年第4期12-22,共11页Journal of Modern Information

基　　金：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)。

摘　　要：[目的/意义]当前基于知识图谱的主流推荐算法主要对项目侧知识进行挖掘利用,较少关注用户侧的辅助信息,存在用户数据稀疏和挖掘深度不够等问题。[方法/过程]针对用户侧和项目侧辅助信息的结构及特征差异,提出了一种融合社交关系和知识图谱的双图注意力推荐模型。首先,将用户社交网络图和项目知识图谱分别与用户—项目交互图融合,得到用户社交关系协同图和项目协同图。其次,利用双图注意力网络分别处理这两个知识图谱,提取不同的用户和项目特征向量。然后,通过注意力机制融合得到的用户和项目特征向量。最后,利用向量间的内积运算得到用户和物品的交互概率进行推荐。[结果/结论]在Douban和Last-FM数据集上进行的实验表明,该模型在各个数据集上的性能优于其他基准模型。[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 issues 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 recommendation model that integrated social relationships and knowledge graphs.Firstly,the user social network graph and item knowledge 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 knowledge graphs separately,extracting different user and item feature vectors.Then,through the attention mechanism,the extracted user and item feature vectors were merged.Finally,the interaction probability of users and items was calculated using 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 

分 类 号：G201[文化科学—传播学]