Gold Standard Dataset for the Reviewer Assignment Problem

本研究收集了一个高质量的审稿人专业数据集，名为‘Gold Standard Dataset for the Reviewer Assignment Problem’，由卡内基梅隆大学的研究人员创建。该数据集包含58名研究人员提供的477个自报告专业评分，这些评分基于他们过去一年阅读的计算机科学论文。数据集旨在用于训练和评估相似性计算算法，以改进审稿人与论文的匹配过程。该数据集的应用领域主要集中在提高计算机科学会议的审稿质量，解决自动化审稿分配中的相似性计算问题。

This study collected a high-quality expert reviewer dataset named 'Gold Standard Dataset for the Reviewer Assignment Problem', developed by researchers at Carnegie Mellon University. This dataset includes 477 self-reported expertise ratings from 58 researchers, which are based on the computer science papers they have read over the past year. The dataset is designed for training and evaluating similarity computation algorithms to optimize the reviewer-paper matching process. Its primary applications focus on improving the review quality of computer science conferences and resolving similarity computation problems in automated reviewer assignment workflows.