  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.
