Recommender system for an academic supervisor with a matrix normalization approach

Abstract

This article proposes a recommendation system for choosing an academic supervisor, based on an assessment of the similarity of student interests and the scientific achievements of the possible mentor from the university faculty. We used a new approach to calculate similarity with no creating co-authorship networks but using Scopus quality metrics. Each scientist is presented as a combination of his achievements in each field of science. As a normalization method, we used the cumulative distribution function of the logarithm of the weighted impacts of professors in the field. We compared different similarity measures and performed clustering to assess their adequacy and thus assess the quality of the system due to the impossibility of comparing the received recommendations with the data of the past years.