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title: 'A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions'
tags: [Survey]

---

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> [Paper link](https://arxiv.org/abs/2109.12843) | [Note link](https://zhuanlan.zhihu.com/p/670305173) | [Code link](https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems) | ACM 2023

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**Thoughts**
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## Abstract

In this survey, they conduct a comprehensive review of the literature on graph neural network-based recommender systems. They 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.

## Background

![image](https://hackmd.io/_uploads/Hy1P02oPp.png)

![image](https://hackmd.io/_uploads/Hk1tC2iPp.png)
