Reviewer Selection

Reviewer selection, crucial across diverse fields from academic publishing to software engineering and hiring, aims to optimize the matching of submissions or candidates with appropriate evaluators. Current research focuses on developing automated systems using machine learning, including deep learning models like feed-forward and graph convolutional neural networks, to improve the accuracy and efficiency of reviewer assignment based on factors such as expertise, workload, and past performance. These advancements address challenges like bias, scalability, and the need for explainable recommendations, ultimately improving the quality and fairness of evaluation processes across various domains.