Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (1): 176-184.DOI: 10.3778/j.issn.1673-9418.2103072

• Artificial Intelligence • Previous Articles     Next Articles

Double End Knowledge Graph Convolutional Networks for Recommender Systems

LI Xiang, YANG Xingyao+(), YU Jiong, QIAN Yurong, ZHENG Jie   

  1. School of Software, Xinjiang University, Urumqi 830008, China
  • Received:2021-03-22 Revised:2021-06-15 Online:2022-01-01 Published:2021-06-17
  • About author:LI Xiang, born in 1996, M.S. candidate, member of CCF. His research interest is recommender system.
    YANG Xingyao, born in 1984, Ph.D., associate professor, member of CCF. His research interests include recommender system, big data and trust computation.
    YU Jiong, born in 1964, Ph.D., professor, Ph.D. supervisor, member of CCF. His research interests include grid computing and parallel computing.
    QIAN Yurong, born in 1980, Ph.D., professor, Ph.D. supervisor, member of CCF. Her research interests include image processing, artificial intelligence and network computing.
    ZHENG Jie, born in 1989, M.S. candidate. His research interest is recommender system.
  • Supported by:
    National Natural Science Foundation of China(61862060);National Natural Science Foundation of China(61966035);National Natural Science Foundation of China(61562086);Education Department Project of Xinjiang Uygur Autonomous Region(XJEDU2016S035);Doctoral Research Start-up Foundation of Xinjiang University(BS150257)


李想, 杨兴耀+(), 于炯, 钱育蓉, 郑捷   

  1. 新疆大学 软件学院,乌鲁木齐 830008
  • 通讯作者: + E-mail:
  • 作者简介:李想(1996—), 男, 湖北随州人, 硕士研究生, CCF会员,主要研究方向为推荐系统。
  • 基金资助:


Knowledge graph (KG) provides a data structure to generate hybrid recommendations based on content and collaborative filtering. However, the existing recommendation methods based on knowledge graph take much less account of the user attribute information than the item attribute. To solve this problem, double end knowledge graph convolutional networks (DEKGCN) for recommender systems is proposed. In this algorithm, certain amount of sample of each entity’s neighborhood in the knowledge graph is taken as its high-order acceptance domain, and the related attributes of users in the dataset are taken as its first-order receptive field. Then, when calculating the representation of a given entity and a user, the neighborhood information is combined respectively, and finally the probability of user’s preference for items is obtained. It is an end-to-end framework that integrates multiple information of both user and item sides to learn the vector representation of users and items, which effectively solves the problem of data sparsity and cold start. Experimental results on real datasets show that DEKGCN has better recommendation quality than other baselines.

Key words: recommender system, knowledge graph (KG), graph convolutional networks, knowledge representation learning



关键词: 推荐系统, 知识图谱(KG), 图卷积网络, 知识表示学习

CLC Number: