CLOSED: Call for Papers: Special Issue on Graph Machine Learning for Recommender Systems (GML4Rec)

IEEE TBD seeks submissions for this upcoming special issue.

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.