摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-机器学习的新研究-计算智能是一篇报告的主题。根据NewsRx记者在合肥的新闻报道,研究表明:“在同行评审中,论文推荐是匹配审稿人的有效方法。但现有推荐方法要么对论文过程敏感,要么总体上不公平。”本研究经费来源于国家自然科学基金(NSFC)。为此，本文提出了一种基于深度ASPEC T和广度ASPEC T的GreedY版本自动阅卷算法GMCTS，具体地说，从深度ASPEC T出发，提出了一种基于深度ASPEC T的GreedY版本自动阅卷算法GMCTS。本文重点研究了每项建议中论文与审稿人之间的最大权重匹配，从广度方面考虑了推荐审稿人所涵盖的最广泛的区分主题(专业知识)，并从理论和实验上考虑了论文需求约束、审稿人工作量约束和多种利益冲突类型(COIs)。"在基准数据集上进行的大量实验表明,GMCTS具有良好的性能."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning-Computational Intelligence is the subject of a report. According to news reporting from Hefei, People's Republic of China, by NewsRx journalists, research stated, "Paperrevi ewer recommendation is an effective method to match reviewers for papers in peer review. However, existing recommendation methods are either sensitive to the or der of paper process or not excepted fair in total." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from the Hefei Univer sity of Technology, "How to efficiently and accurately recommend is still a toug h task and needs to be further explored. Thus, in this paper, we propose a greed y-version automatic paper-reviewer recommendation algorithm regarding both aspec ts of depth and breadth, named as GMCTS. More specifically, from the depth aspec t, we focus on the maximum weight matching between paper and reviewer for each r ecommendation. While from the breadth aspect, we consider the broadest of distin ctive topics (expertise) covered by the recommended reviewers. Furthermore, to a void the unfairness of recommendation, we consider three types of constraints bo th theoretically and experimentally, including paper demand constraint, reviewer workload constraint and multiple types of Conflict Of Interests (COIs). Finally , extensive experiments conducted on benchmark datasets demonstrate that GMCTS a chieves an impressive performance."
NETL
NSTL