@misc{10481/92284,
year = {2024},
month = {1},
url = {https://hdl.handle.net/10481/92284},
abstract = {In the context of academic expert finding, this paper investigates and compares the
performance of information retrieval (IR) and machine learning (ML) methods, including deep
learning, to approach the problem of identifying academic figures who are experts in different
domains when a potential user requests their expertise. IR-based methods construct multifaceted
textual profiles for each expert by clustering information from their scientific publications. Several
methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods
treat expert finding as a classification task, training automatic text classifiers using publications
authored by experts. By comparing these approaches, we contribute to a deeper understanding of
academic-expert-finding techniques and their applicability in knowledge discovery. These methods
are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results
show how IR techniques were, in general, more robust with both datasets and more suitable than the
ML-based ones, with some exceptions showing good performance.},
organization = {Spanish “Agencia Estatal de Investigación” under
grants PID2019-106758GB-C31 and PID2020-113230RB-C22},
organization = {Spanish “FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades” under
grant A-TIC-146-UGR20},
organization = {European Regional Development Fund (ERDF-FEDER)},
publisher = {MDPI},
keywords = {Expert finding},
keywords = {Information retrieval},
keywords = {Machine learning},
title = {Information Retrieval and Machine Learning Methods for Academic Expert Finding},
doi = {10.3390/a17020051},
author = {de Campos, Luis M. and Fernández Luna, Juan Manuel and Huete Guadix, Juan Francisco and Ribadas Pena, Francisco J. and Bolaños, Néstor},
}