reviewACM Computing SurveysMay 5, 2022Closed access

Graph Neural Networks in Recommender Systems: A Survey

Peking University · Alibaba Group (China)

Indexed incrossref

Abstract

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on…

Citation impact

1,123
total citations
FWCI
325.29
Percentile
100%
References
234
Citations per year

Authors

5

Topics & keywords

Keywords
  • Recommender system
  • Computer science
  • Information overload
  • Graph
  • Implementation
  • Field (mathematics)
  • Key (lock)
  • Information retrieval
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