Reviewer Recommender

Reviewer Recommender is a reviewer recommendation system for research papers, developed by the Knowledge Engineering Group (KEG) at Tsinghua University. It comes as a Chrome Extension designed to work seamlessly with the ScholarOne Reviewer System, so editors can find reviewers without leaving their familiar workflow.

Once installed, Reviewer Recommender automatically extracts paper information, including the paper content and author details, directly from the web page. There is no need to copy, paste, or re-enter anything by hand, which saves time and reduces errors during manuscript handling.

Based on the extracted information, Reviewer Recommender taps into the academic big data from Aminer (https://aminer.org/) to identify and recommend the most suitable reviewers for each paper. By matching papers with experts who truly fit their topics, it helps editors assemble qualified reviewer lists faster and with greater confidence.

If you manage peer review on ScholarOne and struggle with finding the right experts for every submission, Reviewer Recommender offers a practical, data-driven way to streamline the recommendation process.