A Survey on Recommender Systems Using Graph Neural Network

2024/09/06 by Vineeta Anand, V. D. Anand, Ashish Kumar Maurya · 5 citations
Computer Science · #Advanced Graph Neural Networks #Data Stream Mining Techniques #Recommender Systems and Techniques

paper · doi:10.1145/3694784

openalex publication_date 2024/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25

Abstract

The expansion of the Internet has resulted in a change in the flow of information. With the vast amount of digital information generated online, it is easy for users to feel overwhelmed. Finding the specific information can be a challenge, and it can be difficult to distinguish credible sources from unreliable ones. This has made recommender system (RS) an integral part of the information services framework. These systems alleviate users from information overload by analyzing users’ past preferences and directing only desirable information toward users. Traditional RSs use approaches like collaborative and content-based filtering to generate recommendations. Recently, these systems have evolved to a whole new level, intuitively optimizing recommendations using deep network models. graph neural networks (GNNs) have become one of the most widely used approaches in RSs, capturing complex relationships between users and items using graphs. In this survey, we provide a literature review of the latest research efforts done on GNN-based RSs. We present an overview of RS, discuss its generalized pipeline and evolution with changing learning approaches. Furthermore, we explore basic GNN architecture and its variants used in RSs, their applications, and some critical challenges for future research.

Citations

• Matrix Factorization Techniques for Recommender Systems
• Inductive Representation Learning on Large Graphs
• Discrete Factorization Machines for Fast Feature-based Recommendation
• A Comprehensive Survey on Graph Neural Networks
• Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
• Simplifying Graph Convolutional Networks
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
• Learning to Simulate Complex Physics with Graph Networks
• LightGCN
• Bias and Debias in Recommender System: A Survey and Future Directions
• Graph neural networks: A review of methods and applications
• A Comprehensive Survey on Graph Neural Networks
• Graph Neural Networks Designed for Different Graph Types: A Survey
• Explainability in music recommender systems
• SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation

Cited by

• FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimization
• GraphTorque: Torque-Driven Rewiring Graph Neural Network
• LPS-GNN : Deploying Graph Neural Networks on Graphs with 100-Billion Edges
• SLIF-MR: Self-loop Iterative Fusion of Heterogeneous Auxiliary Information for Multimodal Recommendation
• When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation

Related

• Graph Neural Networks in Recommender Systems: A Survey
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
• Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
• Graph Foundation Models for Recommendation: A Comprehensive Survey
• Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation