Matching Papers and Reviewers at Large Conferences
This addresses the challenge of efficiently and fairly matching reviewers to papers at scale for conference organizers, though it is incremental as it builds on existing automated matching methods.
The paper tackles the problem of automated reviewer-paper matching for large AI conferences by deploying a novel approach with three elements: data processing, optimization, and a two-phase reviewing process, which was adopted by multiple conferences like AAAI 2021 and ICML 2022, and evaluated through real-data analysis showing improvements over previous methods.
Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper studies a novel reviewer-paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022, and IJCAI 2022. This approach has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer-paper scores; (2) formulating and solving an optimization problem to find good reviewer-paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data -- including a comparison with the matching algorithm used in AAAI's previous (2020) iteration -- and supplements this with additional numerical experimentation.