Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (12): 156-162.DOI: 10.3778/j.issn.1002-8331.1908-0212

Previous Articles     Next Articles

TWD-GNN:Recommendation Method of Graph Neural Network Based on Three-Way Decision

LI Xian, ZHANG Zehua, ZHAO Xia, TIAN Hua   

  1. College of Information and Computer, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
  • Online:2020-06-15 Published:2020-06-09



  1. 太原理工大学 信息与计算机学院,山西 晋中 030600


With increasing in scales of social networks, modeling complex graphs come to be a challenge for cold start recommendations. The conflicts involving in these complex information will directly affect the recommendation results. So the paper proposes a Graph Neural Networks recommendation algorithm based on Three-Way Decision theory, named as TWD-GNN. Three-way decision is introduced to divide the entire data set into tri-partitions as positive, boundary, and negative domains. And solving local conflicts can benefit from the progress, which the auxiliary information from multi-sources is integrated with the boundary domain. The graph neural network method is available for effectively mining information, reconstructing the network and predicting the scores for recommendations. The experimental results show that the proposed algorithm can reflect user preferences more accurately than the traditional collaborative filtering recommendation algorithms, and and improve the recommendation quality.

Key words: recommendation system, information conflict, graph neural network, three-way decision



关键词: 推荐系统, 信息冲突, 图神经网络, 三支决策