Graph Neural Networks in Recommender Systems: A Survey

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

Accepted by ACM Computing Surveys (CSUR)

Cited by in corpus (17)

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• Automated Self-Supervised Learning for Recommendation
• Graph-less Collaborative Filtering
• PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
• Model Degradation Hinders Deep Graph Neural Networks
• HongTu: Scalable Full-Graph GNN Training on Multiple GPUs (via communication-optimized CPU data offloading)
• CLDG: Contrastive Learning on Dynamic Graphs
• User Consented Federated Recommender System Against Personalized Attribute Inference Attack
• View-based Explanations for Graph Neural Networks
• Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
• Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
• FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
• LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation
• Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation