A survey of multi-scenario applications of graph neural network in recommendation system
XING Xing
LIU Jiawen
WANG Tianchi
WANG Hongda
JIA Zhichun
Abstract：The recommendation system can quickly and effectively obtain valuable information from complex data.Traditional recommendation is limited in terms of new users and new projects,and data scarcity makes it difficult to recommend,while ignoring the evolution of user interest.In view of the limitations of traditional recommendation methods in the face of complex,large-scale and dynamic recommendation scenarios,graph neural network technology has attracted wide attention in academia.Graph neural networks have advantages in dealing with graph data and complex interactions,and can improve the personalization,interpretability and timeliness of recommendations.Based on the interaction between graph structure data and nodes,graph neural network uses the characteristics of nodes and neighbor information to recommend,which improves the accuracy of recommendation.Firstly,the basic principle and common models of graph neural network are introduced.Secondly,the problems faced by the current graph neural network in recommendation are analyzed,and the countermeasures are discussed.Finally,according to different recommendation scenarios,the coping methods in the existing research are classified and summarized.
Keywords：recommendation systemsgraph neural networkrecommendation scenario
Publication Date：2023-12-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 368-375 )