Computer Science ›› 2022, Vol. 49 ›› Issue (9): 1-13.doi: 10.11896/jsjkx.210900072

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

Survey of Recommender Systems Based on Graph Learning

CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan   

  1. School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China
  • Received:2021-09-09 Revised:2022-03-28 Online:2022-09-15 Published:2022-09-09
  • About author:CHENG Zhang-tao,born in 1998,postgraduate.His main research interests include machine learning,data mining and recommender systems.
    ZHOU Fan,born in 1981,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include machine lear-ning,spatio-temporal data mining,data mining and knowledge discovery.
  • Supported by:
    National Natural Science Foundation of China(62072077,62176043),National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2019YFB1406202)and Sichuan Science and Technology Program(2020GFW068,2020ZHCG0058,2021YFQ0007,2020YFG0053).

Abstract: Collaborative filtering is a widely used technique in current recommendation systems.It leverages the similarity between different users or items to retrieve interactive information between users and items and recommends new items for target users.In recent years,graph learning has gradually become an emerging recommendation paradigm due to its excellent perfor-mance and scalability in graph representation learning.This paper systematically reviews the most recent research on recommendation field from the perspective of graph learning.First,we provide a taxonomy that groups the current recommendation scenarios into two categories according to the data type used,including recommendation systems based on interactive information that leverage user-item interaction data as the main data source and auxiliary information-enhanced recommendation systems that incorporate social information associated with users and items as well as the knowledge graph information.Then,we review the main approaches,fundamental algorithms and critical difficulties of current recommendation models from the perspectives of random walk,graph representation learning and graph neural networks.Finally,we summarize the main challenges of graph learning methods in the field of recommendation system and outline the possible future research directions.

Key words: Recommender system, Collaborative filtering, Graph learning

CLC Number: 

  • TP181
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