A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

By Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He and 1 more. Trans. Recomm. Syst., 2021

789 citations recorded by the source corpus.

Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories: spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions in this area. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/071e053890765ecc2ff8ef9054e9c75ec135e167

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Junliang Yu
5. Li-zhen Cui
6. Bin Cui
7. Fei Sun
8. Shiwen Wu

In Information Systems

1. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Junliang Yu
5. Li-zhen Cui
6. Bin Cui
7. Fei Sun
8. Shiwen Wu

Citations in the corpus

• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “One recent survey under review [40] classifies the existing works in GNN-based recommender systems from four perspectives of recommender systems, i.”
• Recommender Systems in the Era of Large Language Models (LLMs) (2023, neutral): “......”
• Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation (2021, background): “Graph Neural Networks (GNNs) [6, 30] now have become widely acknowledged powerful architectures for modeling recommendation data.”