Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (2): 55-62.doi: 10.11896/jsjkx.221200169

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

Knowledge Graph and User Interest Based Recommendation Algorithm

XU Tianyue1, LIU Xianhui2, ZHAO Weidong2   

  1. 1 College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
    2 CAD Research Center,College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
  • Received:2022-12-29 Revised:2023-05-24 Online:2024-02-15 Published:2024-02-22
  • About author:XU Tianyue,born in 1998,master.Her main research interests include know-ledge graph and recommender systems.LIU Xianhui,born in 1979,Ph.D,associate researcher(associate professor).His main research interests include machine learning,data mining and big data,and networked manufacturing.
  • Supported by:
    National Key Research and Development Program of China(2022YFB3305700).

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Abstract: In order to solve the problems of cold start and data sparsity in the collaborative filtering recommendation algorithm,the knowledge graph with rich semantic information and path information is introduced in this paper.Based on its graph structure,the recommendation algorithm which applies graph neural network to knowledge graph is favored by researchers.The core of the recommendation algorithm is to obtain item features and user features,however,research in this area focuses on better expressing item features and ignoring the representation of user features.Based on the graph neural network,a recommendation algorithm based on knowledge graph and user interest is proposed.The algorithm constructs user interest by introducing an independent user interest capture module,learning user historical information and modeling user interest,so that it is well represented in both users and items.Experimental results show that on the MovieLens dataset,the recommendation algorithm based on knowledge graph and user interest realizes the full use of data,has good results and promotes the accuracy of recommendation.

Key words: Recommendation algorithm, Knowledge graph, Graph neural networks, User interest

CLC Number: 

  • TP391
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