Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (6): 1343-1353.DOI: 10.3778/j.issn.1673-9418.2110057

• Artificial Intelligence • Previous Articles     Next Articles

Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network

GUO Xiaowang1, XIA Hongbin1,2,+(), LIU Yuan1,2   

  1. 1. School of Artificial Intelligence and Computer, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi, Jiangsu 214122, China
  • Received:2021-10-22 Revised:2022-01-25 Online:2022-06-01 Published:2022-06-20
  • About author:GUO Xiaowang, born in 1996, M.S. candidate.Her research interests include machine learning and recommendation system.
    XIA Hongbin, born in 1972, Ph.D., associate professor, member of CCF. His research interests include personalized recommendation, natural language processing and network optimization.
    LIU Yuan, born in 1967, professor, senior mem-ber of CCF. His research interests include network security and social network.
  • Supported by:
    National Natural Science Foundation of China(61972182)


郭晓旺1, 夏鸿斌1,2,+(), 刘渊1,2   

  1. 1. 江南大学 人工智能与计算机学院,江苏 无锡 214122
    2. 江苏省媒体设计与软件技术重点实验室,江苏 无锡 214122
  • 通讯作者: + E-mail:
  • 作者简介:郭晓旺(1996—),女,河南安阳人,硕士研究生,主要研究方向为机器学习、推荐系统。
  • 基金资助:


Concerning the failure of most current recommendation models based on knowledge graph to adequately model users’ characteristics, the neighborhood relationship between entities in the knowledge graph is not considered. This paper proposes a hybrid recommendation model that combines knowledge graph and graph convolutional network (HKC). Firstly, the KGCN (knowledge graph convolutional networks for recommender systems) algorithm is used to capture the correlation between items, and obtain the feature vector of the item through neighborhood aggregation unit. The entities associated with the user in the knowledge graph are extracted through collaborative propagation. Then the model uses the alternate learning method to optimize the model prediction unit and the knowledge graph embedding unit at the same time, and calculate the user’s feature vector through the interaction unit. Finally, the user feature vector and the item feature vector are sent to the prediction link and the interaction probability between the user and the item is calculated through the inner product operation and normalization of the vector. Comparative experiments are conducted on three public datasets with seven baseline models. On the MovieLens-1M dataset, AUC is increased by 0.25% to 37.41%, and ACC is increased by 0.78% to 49.44%; on the Book-Crossing dataset, AUC is increased by 0.04% to 19.38%, and ACC is increased by 6.49% to 18.60%; on the Last.FM dataset, AUC is increased by 1.33% to 33.50%, and ACC is increased by 0.36% to 30.66%. Experimental results show that the model proposed has improved performance compared with other benchmark models.

Key words: recommender system, knowledge graph, alternate learning, neighborhood aggregation



关键词: 推荐系统, 知识图谱, 交替学习, 邻域聚合

CLC Number: