Important Dates
- Submission Deadline: 30 October 2023
Publication: July 2024
Graph Machine Learning for Recommender Systems (GML4Rec) incorporates graph machine learning techniques with conventional RS paradigm in which data (e.g. users and items) for recommendation is constructed as graphs to capture the complex objects relations. Although it is a promising and popular research topic, how to exploit graph machine learning techniques to improve recommendation performance still faces several challenges. First, it is critically important to construct appropriate graphs to model the diverse object relations without losing useful information among the objects. Second, the user-item information propagation and the side information aggregation inevitably result in the requirements of powerful graph machine learning approaches. Furthermore, many other properties of RS are still not well addressed with traditional collaborative filtering based approaches, such as interpretability, robustness, transparency, fairness, self-evolution, and large-scale adaptability.
Recently, graph neural networks, e.g., GCN, GAT and Graph-SAGE, are widely utilized to learn the object features and capture their complex interactions in recommender systems, and achieved promising performance gains. As such, graph construction, information propagation and aggregation, and many other properties in GML4Rec have attracted rising research interests. In this special issue, we aim to discuss the latest research advances in the theoretical foundations and practical application methods of GML4Rec. We welcome submissions that focus on recent advances in the research/development of GML4Rec along with their applications. Theory and methodology papers are welcome from any of the following areas, including but not limited to the following topics:
Potential topics to be covered in this special issue include, but are not limited to:
- Graph machine learning enhanced collaborative filtering
- Graph machine learning for sequential recommendation
- Graph machine learning for Recommender Systems by incorporating side information
- Graph machine learning strategies/algorithms for Recommender Systems
- Graph machine learning architectures for Recommender Systems
- Graph machine learning for self-evolutionary Recommender Systems
- Graph machine learning for explainable/interpretable Recommender Systems
- Graph machine learning for robust/fair Recommender Systems
- Online graph machine learning based Recommender Systems
- Benchmark/Toolkit for graph machine learning based Recommender Systems
Submission Guidelines
For author information and guidelines on submission criteria, please visit the TBD’s Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Questions?
Contact the guest editors:
- Senzhang Wang, Central South University, China
- Changdong Wang, Sun Yat-sen University, China
- Di Jin, Tianjin University, China
- Shirui Pan, Griffith University, Australia
- Philip S. Yu, University of Illinois Chicago, USA
