Using academic genealogy for recommending supervisors
Fecha
2021Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión publicada / Argitaratu den bertsioa
Impacto
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10.5220/0010442608850892
Resumen
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 Reco ...
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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. [--]
Materias
Recommender systems,
Academic genealogy,
Academic supervising,
Nearest centroid classification
Editor
SciTePress
Publicado en
Filipe, F.; Smialek, M.; Brodsky, A.; Hammoudi, S. (Eds.): Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021). Scitepress, 2021, pp. 452 - 462, 978-989-758-509-8
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila
Versión del editor
Entidades Financiadoras
This study was supported by CAPES Financial
Code 001, PNPD/CAPES (464880/2019-00), CNPq
(301618/2019-4), and FAPERGS (19/2551-0001279-
9, 19/2551-0001660).