%0 Journal Article %A WU Jing %A XIE Hui %A JIANG Huowen %T Survey of Graph Neural Network in Recommendation System %D 2022 %R 10.3778/j.issn.1673-9418.2203004 %J Journal of Frontiers of Computer Science & Technology %P 2249-2263 %V 16 %N 10 %X

Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well. Graph neural network (GNN) can extract and represent the features from edges and nodes data in the graphs and has inherent advantages in processing the graphs structure data, so it flourishes in recommendation system. This paper sorts out the main references of graph neural network in recommendation system in recent years, focuses on the two perspectives of method and problem, and systematically reviews graph neural network in recommendation system. Firstly, from the method level, five graph neural networks of the recommendation system are elaborated, including the graph convolutional network in the recommendation system, graph attention network in the recommendation system, graph autoencoder in the recommendation system, graph generation network in the recommendation system and graph spatial-temporal network in the recommendation system. Secondly, from the perspective of problem similarity, six major problem types are summarized: sequence recommendation, social recommendation, cross-domain recommendation, multi-behavior recommendation, bundle recommendation, and session-based recommen-dation. Finally, based on the analysis and summary of the existing methods, this paper points out the main difficu-lties in the current research on graph neural network in recommendation system, proposes the corresponding issues that can be investigated, and looks forward to the future research directions on this topic.

%U http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.2203004