Decision Support System for Solving Reviewer Assignment Problem

Abstract

Reviewing is the most important step in the quality accreditation of scientific works, requires the professional expertise of the reviewer as well as there is no conflict of interest in the evaluation process. However, we also acknowledge that reviewers have limited knowledge, experience and opinions about the work of others, so they may misinterpret the author’s point of view, leading to the rejection of excellent scientific work or a potentially successful project proposal. Manually selecting reviewers can lead to bias and time-consuming. To solve these problems, we have developed a support system for selecting a group of reviewers to evaluate a particular problem, such as a proposal or a research paper. Our support system consists of three main modules: data collection, reviewer identification and group prediction of reviewers. The Data Collection module collects data from a variety of sources to create the scientist profile database. The reviewer identification module recognizes reviewers on a specific topic. The reviewer prediction module provides a group of exp reviewers to evaluate a submitted paper or a proposal. Experiments on the DBLP computer science bibliography dataset showed that our system achieves better results in terms of accuracy in comparison to other methods.

Keywords:

• Group reviewers
• group reviewer finding
• review assignment
• reviewer support system

Acknowledgment

This work was supported by the 2020 Yeungnam University Research Grant.

Notes

1 https://www.researchgate.net

2 http://citeseerx.ist.psu.edu/index

3 https://dblp.org

4 https://scholar.google.com

5 https://dblp.org/xml/

6 https://github.com/Lucaweihs/impact-prediction#getting-data