Submission-Aware Reviewer Profiling for Reviewer Recommender System

TL;DR

This paper introduces a novel reviewer profiling method that learns from publication abstracts to improve reviewer matching accuracy, interpretability, and deployment success in top-tier conferences.

Contribution

It proposes a submission-aware reviewer profiling approach that captures explicit reviewer context and introduces a new dataset for evaluation, outperforming existing methods.

Findings

Significant improvement in matching precision.

Enhanced explainability of reviewer recommendations.

Successful deployment at major conferences.

Abstract

Assigning qualified, unbiased and interested reviewers to paper submissions is vital for maintaining the integrity and quality of the academic publishing system and providing valuable reviews to authors. However, matching thousands of submissions with thousands of potential reviewers within a limited time is a daunting challenge for a conference program committee. Prior efforts based on topic modeling have suffered from losing the specific context that help define the topics in a publication or submission abstract. Moreover, in some cases, topics identified are difficult to interpret. We propose an approach that learns from each abstract published by a potential reviewer the topics studied and the explicit context in which the reviewer studied the topics. Furthermore, we contribute a new dataset for evaluating reviewer matching systems. Our experiments show a significant, consistent improvement in precision when compared with the existing methods. We also use examples to demonstrate why our recommendations are more explainable. The new approach has been deployed successfully at top-tier conferences in the last two years.