Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (6): 1133-1144.DOI: 10.3778/j.issn.1673-9418.2008059

• Artificial Intelligence • Previous Articles     Next Articles

Recommendation Algorithm Combining Knowledge Graph and Short-Term Preferences

GAO Yang, LIU Yuan   

  1. 1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. Jiangsu Key Laboratory of Media Design and Software Technology (Jiangnan University), Wuxi, Jiangsu 214122, China
  • Online:2021-06-01 Published:2021-06-03



  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(江南大学),江苏 无锡 214122


In recent years, attention has been paid to knowledge graph as auxiliary information to enhance recom-mendation increasedly. Since the goal of the knowledge graph learning task is to restore the relationship of the triples in the knowledge graph, rather than to accomplish the recommendation task, it is difficult for the knowledge graph learning task to efficiently help the recommendation task improve the recommendation performance. In addition, user??s interest is easily affected by short-term environment and mood. This paper proposes a recommendation model that is a multi-task feature learning approach for knowledge graph and short-term preferences enhanced recommendation (MKASR) in response to the above two points. Firstly, the RippleNet algorithm is used to extract the relationship pairs between the user and the knowledge graph entity, and then these relationship pairs are stored in the form of knowledge graph triples for participating in training. The bidirectional GRU (gate recurrent unit) network based on the attention mechanism is adopted from the user??s recent interaction sequence of items to extract the user??s short-term preferences. Secondly, this paper uses the multi-task learning method to train the knowledge graph learning module and the recommendation module. And the feature representation between user and item can be obtained. Finally, these feature representations and the short-term preferences of users are taken into account to make comprehensive recommendations to users. The experiments on real MovieLens-1M and Book-Crossing datasets demonstrate that the proposed model has improved performance compared with other recommendation algorithms in AUC, ACC, Precision and Recall evaluation indexes.

Key words: recommender systems, knowledge graph, short-term preferences, preference propagation, multi-task learning



关键词: 推荐系统, 知识图谱, 短期偏好, 偏好传播, 多任务学习