Peer review is an essential part of scientific communications to ensure the quality of publications and a healthy scientific evaluation process. Assigning appropriate reviewers poses a great challenge for program chairs and journal editors for many reasons, including relevance, fair judgment, no conflict of interest, and qualified reviewers in terms of scientific impact. With a steady increase in the number of research domains, scholarly venues, researchers, and papers in academia, manually selecting and accessing adequate reviewers is becoming a tedious and time-consuming task. Traditional approaches for reviewer selection mainly focus on the matching of research relevance by keywords or disciplines. However, in real-world systems, various factors are often needed to be considered. Therefore, we propose a multilayered approach integrating Topic Network, Citation Network, and Reviewer Network into a reviewer Recommender System (TCRRec). We explore various aspects, including relevance between reviewer candidates and submission, authority, expertise, diversity, and conflict of interest and integrate them into the proposed framework TCRRec. The paper also addresses cold start issues for researchers having unique areas of interest or for isolated researchers. Experiments based on the NIPS and AMiner dataset demonstrate that the proposed TCRRec outperforms state-of-the-art recommendation techniques in terms of standard metrics of precision@k, MRR, nDCG@k, authority, expertise, diversity, and coverage.

同行评审是科学交流的重要组成部分，以确保出版物的质量和健康的科学评估过程。指派适当的审稿人对计划主席和期刊编辑提出了巨大的挑战，原因很多，包括相关性，公正的判断，无利益冲突以及在科学影响方面有资格的审稿人。随着学术界研究领域，学术场所，研究人员和论文数量的稳定增长，手动选择和访问适当的审阅者已成为一项繁琐而耗时的任务。审稿人选择的传统方法主要集中在关键词或学科对研究相关性的匹配上。但是，在实际系统中，经常需要考虑各种因素。因此，我们提出了一种将主题网络，引文网络和审阅者网络集成到审阅者推荐系统（TCRRec）中的多层方法。我们探索了各个方面，包括审稿人候选人与投稿之间的相关性，权威，专业知识，多样性和利益冲突，并将它们整合到了拟议的框架TCRRec中。本文还针对具有独特兴趣领域的研究人员或孤立的研究人员解决了冷启动问题。基于NIPS和AMiner数据集的实验表明，在Precision @ k，MRR，nDCG @ k，权限，专业知识，多样性和覆盖率等标准指标方面，建议的TCRRec优于最新的推荐技术。包括审稿人候选人与投稿，权限，专业知识，多样性和利益冲突之间的相关性，并将其整合到拟议框架TCRRec中。本文还针对具有独特兴趣领域的研究人员或孤立的研究人员解决了冷启动问题。基于NIPS和AMiner数据集的实验表明，在Precision @ k，MRR，nDCG @ k，权限，专业知识，多样性和覆盖率等标准指标方面，建议的TCRRec优于最新的推荐技术。包括审稿人候选人与投稿，权限，专业知识，多样性和利益冲突之间的相关性，并将其整合到拟议框架TCRRec中。本文还针对具有独特兴趣领域的研究人员或孤立的研究人员解决了冷启动问题。基于NIPS和AMiner数据集的实验表明，在Precision @ k，MRR，nDCG @ k，权限，专业知识，多样性和覆盖率等标准指标方面，建议的TCRRec优于最新的推荐技术。