Graph Neural Networks in Recommender Systems: A Survey
- URL: http://arxiv.org/abs/2011.02260v4
- Date: Sat, 2 Apr 2022 07:02:02 GMT
- Title: Graph Neural Networks in Recommender Systems: A Survey
- Authors: Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui
- Abstract summary: In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information.
Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems.
This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems.
- Score: 21.438347815928918
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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 GNN-based recommender systems.
Specifically, we provide a taxonomy of GNN-based recommendation models
according to the types of information used and recommendation tasks. Moreover,
we systematically analyze the challenges of applying GNN on different types of
data and discuss how existing works in this field address these challenges.
Furthermore, we state new perspectives pertaining to the development of this
field. We collect the representative papers along with their open-source
implementations in https://github.com/wusw14/GNN-in-RS.
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