Computer Science ›› 2021, Vol. 48 ›› Issue (4): 104-110.doi: 10.11896/jsjkx.200800027

• Database & Big Data & Data Science • Previous Articles     Next Articles

Top-N Recommendation Method for Graph Attention Based on Multi-level and Multi-view

LIU Zhi-xin, ZHANG Ze-hua, ZHANG Jie   

  1. School of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China
  • Received:2020-06-24 Revised:2020-10-01 Online:2021-04-15 Published:2021-04-09
  • About author:LIU Zhi-xin,born in 1996,postgradua-te.His main research interests include recommendation system and knowledge discovery on graph.(itsliuzhixin@163.com)
    ZHANG Ze-hua,born in 1981,Ph.D,master supervisor,is a member of China Computer Federation.His main research interests include granular computing,uncertain reasoning and knowledge discovery on graph.
  • Supported by:
    National Natural Science Foundation of China(61503273,61702356),Industry-University Cooperation Education Program of the Ministry of Education and Shanxi Scholarship Council of China.

Abstract: Recommendation system is a research hotspot in the field of data mining.Due to the emergence of massive data,the reco-mmendation methodsof multi-source information fusion receive great attention.However,the existing recommendation methods based on heterogeneous information fusion often ignore the interaction information between users and items,as well as the interaction between meta-paths in feature representation.Therefore,considering the influence of different perspectives of attribute node embedding and structural meta-paths,a network recommendation method with multi-level graph attention is proposed.This method granulates the multi-source information network structure into multiple independent coarse-grained networks by constructing different meta-paths.Then,based on graph attention mechanism and local node attribute embedding,this method can learn the potential features of users and items separately.Finally,it gives a fine-grained network recommendation after fusion.The horizontal and vertical evaluations are conducted on real large-scale data sets,and the experimental results show that this method can effectively improve the recommendation performance.

Key words: Graph attention network, Hierarchical granulation, Multi-source information fusion, Top-N recommendation

CLC Number: 

  • TP183
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