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

IEEE TBD seeks submissions for this upcoming special issue.
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Submissions Due: 30 October 2023

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.


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