Reviewer Recommender

(keg), data extension tsinghua developed and from recommender是清华大学计算机科学与技术系知识工程实验室（keg）开发的国际学术论文评审专家推荐系统插件。该插件在学术论文审稿常用平台scholarone系统上开发，能通过网页自动提取论文内容及作者信息，基于aminer系统（https://aminer.org/）海量学术专家数据，为每篇论文推荐最合适的审稿人。 reviewer reviewer is works which on a reviewer on reviewers research recommendation it information, it university. proper scholarone extract automatically aminer reviewer recommender by big system. academic is system paper knowledge (https://aminer.org/). a can paper engineering recommend chrome based group