A Group Recommender System for Selecting Experts to Review a Specific Problem

Abstract

With the increase in the number of publications and scientific projects, its quality requirements are increasingly needed. Reviewing is the most important step in accrediting the quality of scientific work. Criteria such as independence, competence, and lack of conflicts of interest in an expert are essential in the reviewer selection process. However, we also know that experts have limited knowledge, experience, and opinions about the work of others, so they might misunderstand the viewpoints of the authors, which may lead to rejection of an excellent scientific work or an implicitly successful project proposal. Manually selecting reviewers can be a biased and time-consuming process. In order to solve these problems, we developed a recommender system to choose a group of experts to evaluate a specific problem, such as a research proposal or paper. Our recommender system consists of three main modules: data collection, expert detection, and expert prediction. The data collection module is to collect data from various sources to create a database of scientist profiles. The expert detection module is used to determine the experts on each particular topic. The expert prediction module is to provide a list of experts to answer the query. We conducted experiments with the DBLP Computer Science Bibliography dataset, and the results show that our system is an up-and-coming selection process.