Automated reviewer recommendation for scientific conferences currently relies on the assumption that the program committee has the necessary expertise to handle all submissions. However, topical discrepancies between received submissions and reviewer candidates might lead to unreliable reviews or overburdening of reviewers, and may result in the rejection of high-quality papers. In this work, we present DiveRS, an explainable flow-based reviewer assignment approach, which automatically generates reviewer assignments as well as suggestions for extending the current program committee with new reviewer candidates. Our algorithm focuses on the diversity of the set of reviewers assigned to papers, which has been mostly disregarded in prior work. Specifically, we consider diversity in terms of professional background, location and seniority. Using two real world conference datasets for evaluation, we show that DiveRS improves diversity compared to both real assignments and a state-of-the-art flow-based reviewer assignment approach. Further, based on human assessments by former PC chairs, we find that DiveRS can effectively trade off some of the topical suitability in order to construct more diverse reviewer assignments.

翻译：科学会议自动审查者建议科学会议自动化审查者建议目前所依据的假设是,方案委员会拥有处理所有提交材料的必要专门知识。然而,收到的提交材料和审查者候选人之间的时事差异可能导致审查者审查不可靠或审查者负担过重,并可能导致拒绝高质量文件。在这项工作中,我们介绍了可解释的流动审查员分配办法DiveRS, 这种方法自动产生审查员分配以及扩大现有方案委员会、增加新的审查员候选人的建议。我们的算法侧重于分配给文件的一组审查员的多样性,而以前的工作大多忽视了这种多样性。具体地说,我们考虑了专业背景、地点和资历的多样性。我们利用两个真正的世界会议数据集进行评价,我们表明DiveRS改善了多样性,与实际任务和最新的流动审查员分配办法相比。此外,根据前PC主席的人类评估,我们发现DiveRS可以有效地交换一些专题的适合性,以构建更多样化的审查员任务。