FairColor: An efficient algorithm for the Balanced and Fair Reviewer Assignment Problem

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Khadra Bouanane , Abdeldjaouad Nusayr Medakene , Abdellah Benbelghit , Samir Brahim Belhaouari
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Abstract

As the volume of submitted papers continues to rise, ensuring a fair and accurate assignment of manuscripts to reviewers has become increasingly critical for academic conference organizers. Given the paper-reviewer similarity scores, this study introduces the Balanced and Fair Reviewer Assignment Problem (BFRAP), which aims to maximize the overall similarity score (efficiency) and the minimum paper score (fairness) subject to coverage, load balance, and fairness constraints. Addressing the challenges posed by these constraints, we conduct a theoretical investigation into the threshold conditions for the problem’s feasibility and optimality. To facilitate this investigation, we establish a connection between BFRAP, defined over m reviewers, and the Equitable m-Coloring Problem. Building on this theoretical foundation, we propose FairColor, an algorithm designed to retrieve fair and efficient assignments. We compare FairColor to Fairflow and FairIR, two state-of-the-art algorithms designed to find fair assignments under similar constraints. Empirical experiments were conducted on four real and two synthetic datasets involving (paper, reviewer) matching scores ranging from (100,100) to (10124,5880). Results demonstrate that FairColor is able to find efficient and fair assignments quickly compared to Fairflow and FairIR. Notably, in the largest instance involving 10,124 manuscripts and 5680 reviewers, FairColor retrieves fair and efficient assignments in just 67.64 s. This starkly contrasts both other methods, which require significantly longer computation times (45 min for Fairflow and 3 h 24 min for FairIR), even on more powerful machines. These results underscore FairColor as a promising alternative to current state-of-the-art assignment techniques.

FairColor:平衡与公平审稿人分配问题的高效算法
随着提交论文数量的不断增加,确保将稿件公平、准确地分配给审稿人对学术会议组织者来说越来越重要。考虑到论文与审稿人的相似度得分,本研究提出了平衡与公平审稿人分配问题(BFRAP),其目标是在覆盖范围、负载平衡和公平性约束条件下,最大化总体相似度得分(效率)和最小化论文得分(公平性)。为了应对这些约束带来的挑战,我们对问题可行性和最优性的阈值条件进行了理论研究。为便于研究,我们在定义为 m 个审稿人的 BFRAP 与公平 m 染色问题之间建立了联系。在此理论基础上,我们提出了 FairColor,一种旨在检索公平高效分配的算法。我们将 FairColor 与 Fairflow 和 FairIR 进行了比较,这两种最先进的算法是为了在类似的约束条件下找到公平的任务分配而设计的。我们在四个真实数据集和两个合成数据集上进行了实证实验,涉及的(论文、审稿人)匹配分数范围从(100,100)到(10124,5880)不等。结果表明,与 Fairflow 和 FairIR 相比,FairColor 能够快速找到高效、公平的分配。值得注意的是,在涉及 10124 篇稿件和 5680 名审稿人的最大实例中,FairColor 仅用了 67.64 秒就检索到了公平高效的分配。这与其他两种方法形成了鲜明对比,后者需要的计算时间要长得多(Fairflow 需要 45 分钟,FairIR 需要 3 小时 24 分钟),即使在更强大的机器上也是如此。这些结果表明,FairColor 是目前最先进的分配技术的一个很有前途的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
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