Graph Neural Networks in Recommender Systems: A Survey

By Shiwen Wu, Fei Sun, Fei Sun, Bin Cui. ACM Computing Surveys, 2020

1,821 citations recorded by the source corpus.

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://github.com/wusw14/GNN-in-RS.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/3443efc855cebd17d1512d1a703b6e9ee2e4da8b

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. Xiangnan He
2. Hongzhi Yin
3. Tong Chen
4. Tat-seng Chua
5. Yong Li
6. Chen Gao
7. Junliang Yu
8. Xin Xia

In Information Systems

1. Xiangnan He
2. Hongzhi Yin
3. Tong Chen
4. Tat-seng Chua
5. Yong Li
6. Chen Gao
7. Junliang Yu
8. Xin Xia

Citations in the corpus

• A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation (2022, background): “The surge of research on graph-based recommendations [26, 29, 33, 35] inspires a line of work [22, 24, 28, 31] that exploits the power of graph neural networks (GNNs) to capture the high-order semantics between multimodal features and user-item interactions.”
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022, methodology): “For graph collaborative filtering, the construction of neighborhood is more important than other collaborative filtering methods [36], since it is based on the graph structure.”
• Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “Graph Neural Networks [85, 251, 266, 285] and corresponding representation learning [86] techniques have achieved great success [14, 231, 250, 258, 264, 288] in many areas, including modeling molecule graph in various tasks ranging from property prediction [59, 71] to molecule generation [105, 111, ”
• Bootstrap Latent Representations for Multi-modal Recommendation (2022, methodology): “With a surge of research on graph-based recommendations [32, 37, 47], another line of research uses Graph Neural Networks (GNNs) to exploit item multi-modal information and enhance the learning of user and item representations [31, 33, 34].”
• Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation (2021, methodology): “Despite the different implementations in details, these GCN-driven models share a common idea that is to acquire the information from the neighbors in the user-item graph layer by layer to refine the target node’s embeddings and fulfill graph reasoning [29].”
• Advances and Challenges in Conversational Recommender Systems: A Survey (2021, positive): “are survey papers focusing on certain cutting-edge aspects in recommender systems, such as the bias issues and debiasing methods [24], explainability/interpretability [249], evaluation issues [179], and novel methods that leverage deep neural networks [247, 225, 226], knowledge graphs [61], or reinf”
• A Survey of Graph Neural Networks for Social Recommender Systems (2022, background): “The other related surveys [12, 19, 96, 107] focus on graph-based recommender systems, but they partially cover SocialRS.”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “Furthermore, in addition to Reference [202], there are some other surveys closely relevant to this survey as follows:”
• Recommender Systems in the Era of Large Language Models (LLMs) (2023, neutral): “......”
• XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (2022, background): “In recent years, graph neural networks (GNNs) [45], [46] have brought the regime of DNNs to an end [47], [48], [49], and become a routine in recommender systems for its extraordinary ability to model the user behavior data [50], [51].”