首页|Findings from Hefei University of Technology Provides New Data about Computation al Intelligence (An Automatic Paper-reviewer Recommendation Algorithm Based On D epth and Breadth)
Findings from Hefei University of Technology Provides New Data about Computation al Intelligence (An Automatic Paper-reviewer Recommendation Algorithm Based On D epth and Breadth)
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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."
HefeiPeople's Republic of ChinaAsiaComputational IntelligenceMachine LearningAlgorithmsHefei University of T echnology
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