Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (4): 18-29.DOI: 10.3778/j.issn.1002-8331.2205-0268

• Research Hotspots and Reviews • Previous Articles     Next Articles

Research Advances on Graph Neural Network Recommendation of Knowledge Graph Enhancement

WU Guodong, WANG Xueni, LIU Yuliang   

  1. School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
  • Online:2023-02-15 Published:2023-02-15



  1. 安徽农业大学 信息与计算机学院,合肥 230036

Abstract: The existing recommendation methods are mainly based on the users’ historical interaction behavior, and the user and item-related feature information are not fully utilized, resulting in the effect of the recommendation is not ideal. The graph neural network(GNN) recommendation enhanced by knowledge graph(KG) is based on the interaction graph constructed by user and item interaction behavior, and the knowledge graph with the same graph structure is introduced and processed by the graph neural network technology, so as to realize personalized recommendation. In this paper, the research progress of graph neural network recommendation enhanced by existing knowledge graph is discussed. Firstly, on the basis of the discussion of graph neural network recommendation and knowledge graph recommendation, the relevant research results of graph neural network recommendation enhanced by the current knowledge graph are deeply analyzed from the aspects of item knowledge graph and collaborative knowledge graph. Then, the main problems in the graph neural network recommendation research based on the existing knowledge graph enhancement are pointed out from the aspects of large-scale dynamic knowledge graph processing, user preference mining for item attributes, knowledge graph embedding learning problem and so on. Finally, the main research directions of GNN recommendation enhanced by knowledge graph in the future are predicted from the following aspects:GNN recommendation enhanced by knowledge graph in dynamic sequential sequence, GNN recommendation enhanced by knowledge graph in meta-learning, GNN recommendation enhanced by multi-model knowledge graph, GNN cross-domain recommendation enhanced by knowledge graph and so on.

Key words: knowledge graph, graph neural network, recommendation system, item knowledge graph, collaborative knowledge graph

摘要: 已有推荐方法主要基于用户与项目的历史交互行为,未充分运用用户及项目相关特征信息,推荐效果并不理想。知识图谱(knowledge graph,KG)增强的图神经网络(graph neural network,GNN)推荐,是以用户与项目交互行为构建的交互图为基础,引入同为图结构的知识图谱,并运用图神经网络技术进行处理,从而实现个性化推荐。深入探讨了现有知识图谱增强的图神经网络推荐研究进展。首先在对图神经网络推荐和知识图谱推荐进行探讨的基础上,从项目知识图谱和协同知识图谱视角,深入分析了当前知识图谱增强的图神经网络推荐取得的相关研究成果;然后从大规模动态知识图谱处理、用户对项目属性的偏好挖掘、知识图谱的图嵌入学习等方面,指出了已有知识图谱增强的图神经网络推荐研究存在的主要问题;最后从动态时序知识图谱增强的GNN推荐、元学习的知识图谱增强GNN推荐、多模态知识图谱增强的GNN推荐、知识图谱增强的GNN跨领域推荐等方面,展望了知识图谱增强的图神经网络推荐未来主要研究方向。

关键词: 知识图谱, 图神经网络, 推荐系统, 项目知识图谱, 协同知识图谱