About This Document

- 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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