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Authors: Gabriel Madeira 1 ; Eduardo Borges 1 ; Giancarlo Lucca 1 ; Washington Carvalho-Segundo 2 ; Jonata Wieczynski 1 ; Helida Santos 1 and Graçaliz Dimuro 1 ; 3

Affiliations: 1 Centro de Ciências Computacionais, Universidade Federal do Rio Grande, Rio Grande, RS, Brazil ; 2 Instituto Brasileiro de Informação em Ciência e Tecnologia, Brasília, DF, Brazil ; 3 Departamento de Estadística, Informática y Matemáticas, Universidad Publica de Navarra, Pamplona, Spain

Keyword(s): Recommender Systems, Academic Genealogy, Academic Supervising, Nearest Centroid Classification.

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.

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Paper citation in several formats:
Madeira, G.; Borges, E.; Lucca, G.; Carvalho-Segundo, W.; Wieczynski, J.; Santos, H. and Dimuro, G. (2021). Using Academic Genealogy for Recommending Supervisors. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8; ISSN 2184-4992, pages 885-892. DOI: 10.5220/0010442608850892

@conference{iceis21,
author={Gabriel Madeira. and Eduardo Borges. and Giancarlo Lucca. and Washington Carvalho{-}Segundo. and Jonata Wieczynski. and Helida Santos. and Gra\c{C}aliz Dimuro.},
title={Using Academic Genealogy for Recommending Supervisors},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={885-892},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010442608850892},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Using Academic Genealogy for Recommending Supervisors
SN - 978-989-758-509-8
IS - 2184-4992
AU - Madeira, G.
AU - Borges, E.
AU - Lucca, G.
AU - Carvalho-Segundo, W.
AU - Wieczynski, J.
AU - Santos, H.
AU - Dimuro, G.
PY - 2021
SP - 885
EP - 892
DO - 10.5220/0010442608850892

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