| Title | Graph Neural Networks in Recommender Systems: A Survey |
| Authors | Wu, Shiwen Sun, Fei Zhang, Wentao Xie, Xu Cui, Bin |
| Affiliation | Peking Univ, Sch CS, Beijing 100871, Peoples R China Peking Univ, Key Lab High Confidence Software Technol MOE, Beijing 100871, Peoples R China Alibaba Grp, Beijing 100102, Peoples R China Peking Univ Qingdao, Peking Univ, Inst Computat Social Sci, Beijing 100871, Peoples R China |
| Keywords | CONVOLUTIONAL NETWORKS MATRIX FACTORIZATION DIFFERENTIAL PRIVACY |
| Issue Date | Jun-2023 |
| Publisher | ACM COMPUTING SURVEYS |
| 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://githuh.coni/wusw14/GNN-in-RS. |
| URI | http://hdl.handle.net/20.500.11897/661785 |
| ISSN | 0360-0300 |
| DOI | 10.1145/3535101 |
| Indexed | SCI(E) |
| Appears in Collections: | 高可信软件技术教育部重点实验室 |
