Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1681-1705.DOI: 10.3778/j.issn.1673-9418.2112070

• Surveys and Frontiers • Previous Articles     Next Articles

Survey on Applications of Knowledge Graph Embedding in Recommendation Tasks

TIAN Xuan1,+(), CHEN Hangxue1,2   

  1. 1. School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
    2. Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
  • Received:2021-12-17 Revised:2022-02-24 Online:2022-08-01 Published:2022-08-19
  • About author:TIAN Xuan, born in 1976, Ph.D., associate professor. Her research interests include intelligent information processing, text mining, etc.
    CHEN Hangxue, born in 1997, M.S. candidate. Her research interests include intelligent information processing, personalized recommendation, etc.
  • Supported by:
    the National Key Research and Development Program of China(2018YFC1603305)


田萱1,+(), 陈杭雪1,2   

  1. 1. 北京林业大学 信息学院,北京 100083
    2. 国家林业草原林业智能信息处理工程技术研究中心,北京 100083
  • 通讯作者: +E-mail:
  • 作者简介:田萱(1976—),女,山东济宁人,博士,副教授,主要研究方向为智能信息处理、文本挖掘等。
  • 基金资助:


Recommendation systems are designed to recommend personalized content to improve user experience. At present, the recommendation systems still face some challenges such as poor interpretability, cold start problem and serialized recommendation modeling. Recently, the knowledge graph (KG) containing a large amount of semantic and structural information has been widely used in a variety of different recommendation tasks to alleviate the above problems. This paper systematically reviews the innovative applications of knowledge graph embedding (KGE) in different recommendation tasks. It first summarizes three common recommendation tasks and four applying goals of knowledge graph embedding. Then, it generalizes four types of knowledge graph embedding methods according to specific technologies, including traditional embedding method, embedding propagation method, heterogeneous graph embedding method and graph neural network based method. It further elaborates on the applying characteristics and strategies of the above four methods in different recommendation tasks, and evaluates advantages and limitations of each method. Also, it conducts qualitative and quantitative analysis of the associations and differences of four methods from multiple aspects. Finally, it puts forward some views on the development trend of applying knowledge graph embedding for recommendation systems, and proposes several noteworthy development directions in the future from multiple perspectives.

Key words: knowledge graph embedding (KGE), recommendation tasks, explainable recommendation, cold start, serialization recommendation, application of knowledge graph embedding



关键词: 知识图谱嵌入(KGE), 推荐任务, 可解释推荐, 冷启动, 序列化推荐, 知识图谱嵌入应用

CLC Number: