Reviewer Recommender is a research paper reviewer recommendation system developed by Knowledge Engineering Group (KEG), Tsinghua University. This Chrome Extension is designed to work seamlessly with ScholarOne Reviewer System. It offers the ability to automatically extract paper information and recommend suitable reviewers based on academic big data from Aminer.

With Reviewer Recommender, the time-consuming process of finding appropriate reviewers for research papers is simplified. By leveraging the vast academic expert data from Aminer, this extension provides accurate and reliable recommendations for each paper. It eliminates the need for manual search and selection, saving valuable time for researchers and editors.

The integration of Reviewer Recommender with ScholarOne Reviewer System ensures a seamless user experience. Researchers can easily access the extension within the ScholarOne platform, allowing them to effortlessly extract paper details and receive reviewer recommendations in a matter of seconds.

Enhance the efficiency of your research paper review process with Reviewer Recommender. Let this Chrome Extension take care of the reviewer selection, so you can focus on producing high-quality research.