Using academic genealogy for recommending supervisors

Abstract

Selecting an academic supervisor is a complicated task. Masters and Ph.D. candidates usually select the most prestigious universities in a given region, investigate the graduate programs in a research area of interest, and analyze the professors' profiles. This choice is a manual task that requires extensive human effort, and usually, the result is not good enough. In this paper we propose a Recommender System that enables one to choose an academic supervisor based on his/her academic genealogy. We used metadata of different theses and dissertations and applied the nearest centroid model to perform the recommendation. The obtained results showed the high precision of the recommendations, which supports the hypothesis that the proposed system is a useful tool for graduate students.