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Additional Reviewer Assignment by Means of Weighted Association Rules

机译:另外的审阅者通过加权关联规则分配

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摘要

The assessment of the academic papers submitted to scientific journals and conferences is mostly based on peer reviewing. The reviewers assigned to each paper are usually shortlisted from a program committee by matching paper topics with reviewers' expertise. However, reviewer assignments often need to be reconsidered at a later time, because reviewers can be temporarily unavailable or some of the assignments turn out to be critical due to the presence of conflicts. Therefore, there is a need for automated tools that support experts in seeking additional reviewers external to the program committee. In this paper, we propose an association rule-based methodology to recommend additional external reviewers. Weighted Association Rules (WARs), which represent strong associations between paper co-authors, are first extracted from a benchmark publication dataset. Then, they are exploited to discover potential conflicts of interest between co-reviewers as well as between reviewers and authors. Finally, reviewer assignments for papers with conflicts or with low-quality assignments are reconsidered. To tackle this issue, a subset of selected WARs provide promptly usable additional reviewer recommendations, as they highlight external experts who have profitably collaborated with the shortlisted reviewers. The effectiveness of the proposed methodology were validated on both journal and conference datasets.
机译:评估提交给科学期刊和会议的学术论文主要是基于同行评审。分配给每份纸张的审稿人通常通过将纸张主题与审稿人的专业知识相匹配。但是,审阅者分配通常需要在以后重新考虑,因为审阅者可以暂时无法使用,或者某些任务因存在冲突而导致至关重要。因此,需要自动化工具,支持专家在为计划委员会外部寻求其他审核人员。在本文中,我们提出了一种基于结构规则的方法来推荐额外的外部审阅者。在基准发布数据集中首先提取代表纸张共同作者之间强有力的协会的加权关联规则(战争)。然后,他们被剥削以发现共同审阅者与审稿人员和作者之间的潜在利益冲突。最后,重新考虑了与冲突或低质量作业的论文的评论家分配。为了解决这个问题,选定的战争的子集提供了迅速使用的其他审稿人建议,因为它们突出了与入围审稿人有利可图的外部专家。拟议方法的有效性在Journal和会议数据集中验证。

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