Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 220900180-7.doi: 10.11896/jsjkx.220900180

• Big Data & Data Science • Previous Articles     Next Articles

Recommendation Method Based on Knowledge Graph Residual Attention Networks

FAN Hongyu, ZHANG Yongku, MENG Xiangfu   

  1. School of Electronic and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China
  • Published:2023-11-09
  • About author:FAN Hongyu,born in 1997,postgra-duate.Her main research interests include recommendation system and so on.
    MENG Xiangfu,born in 1981,Ph.D,professor,is a senior member of China Compuer Federation.His main research interests include big data analysis and query,spatio-temporal data mining,and machine learning algorithms.
  • Supported by:
    Liaoning Provincial Department of Education Scientific Research Project(LJKZ0355).

Abstract: With the rapid development of the Internet today,recommendation system has become an important means to relieve the information overload.Current recommendation methods mainly use deep learning model to mine users’ interests in the project.However,the current recommendation methods using graph neural networks cannot effectively represent the interaction behaviors between users and items well,and the increase in the number of network layers will cause the problem of gradient disappearance.Therefore,this paper proposes a model that combines the GC-OTE knowledge graph embedding approach with residual networks and attention mechanisms.First,the interaction information of users or items is represented by embedding the neighbor attributes of nodes,then user-item interactions are analyzed by graph neural and residual networks,and finally,attention mechanisms are used to distinguish different neighborhoods.Experiments on two real-world datasets Alibaba-fashion and Last-FM demonstrate that the proposed method can significantly improve the recommendation performance.

Key words: Recommendation system, Graph neural network, Knowledge graph, Collaborative filtering, Embedding propagation

CLC Number: 

  • TP311
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