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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Shiwen Wu
- sl:arxiv_num : 2011.02260
- sl:arxiv_published : 2020-11-04T12:57:47Z
- sl:arxiv_summary : 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 system, there have always been emerging works
in this field. In recent years, graph neural network (GNN) techniques have
gained considerable interests which can naturally integrate node information
and topological structure. Owing to the outperformance of GNN in learning on
graph data, GNN methods have been widely applied in many fields. In recommender
systems, the main challenge is to learn the efficient user/item embeddings from
their interactions and side information if available. Since most of the
information essentially has graph structure and GNNs have superiority in
representation learning, the field of utilizing graph neural network in
recommender systems is flourishing. This article aims to provide a
comprehensive review of recent research efforts on graph neural network based
recommender systems. Specifically, we provide a taxonomy of graph neural
network based recommendation models and state new perspectives pertaining to
the development of this field.@en
- sl:arxiv_title : Graph Neural Networks in Recommender Systems: A Survey@en
- sl:arxiv_updated : 2020-11-04T12:57:47Z
- sl:bookmarkOf : https://arxiv.org/abs/2011.02260
- sl:creationDate : 2020-11-11
- sl:creationTime : 2020-11-11T11:04:40Z
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