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 introduces , 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 2022-2024, and IJCAI 2022-2024. LCM 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.

中文翻译：

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在大型会议上匹配论文和审稿人

同行评审会议是计算机科学领域的主要发表场所，它严重依赖于为每篇论文匹配高素质的审稿人。由于这些会议的规模不断扩大、运作的时间紧迫，以及最近明显不诚实行为的激增，现在除了以自动化方式执行这种匹配之外别无选择。本文介绍了一种新颖的审稿人-论文匹配方法，该方法最近在第 35 届 AAAI 人工智能会议 (AAAI 2021) 上部署，此后已被其他会议（全部或部分）采用，包括 ICML 2022、AAAI 2022-2024、和 IJCAI 2022-2024。 LCM 具有三个主要要素：（1）收集和处理输入数据以识别有问题的匹配并生成审稿人-论文评分； (2) 制定并解决优化问题以找到良好的审稿人-论文匹配； （3）一个两阶段的审查过程，将审查资源从可能被拒绝的论文转移到更接近决策边界的论文。本文还描述了基于对真实数据的广泛事后分析对这些创新的评估，包括与 AAAI 上一次（2020 年）迭代中使用的匹配算法的比较，并通过额外的数值实验对此进行了补充。