RT info:eu-repo/semantics/article
T1 Machine Learning to Predict Recommendation by Tourists in a Spanish Province
A1 Aparicio Castillo, Santiago
A1 Basurto Hornillos, Nuño
A1 Arranz Val, Pablo
A1 Antón Maraña, Paula
A1 Herrero Cosío, Álvaro
K1 Artificial intelligence
K1 Supervised learning
K1 Classification
K1 Tourism management
K1 Recommendation
K1 Turismo
K1 Tourism
K1 Economía
K1 Economics
AB The analysis of the opinions and experiences of tourists is a key issue in tourist promotion. More precisely, forecasting whether a tourist will or will not recommend a given destination, based on his/her profile, is of utmost importance in order to optimize management actions. According to this idea, this research proposes the application of cutting-edge machine learning techniques in order to predict tourist recommendation of rural destinations. More precisely, classifiers based on supervised learning (namely Support Vector Machine, Decision Trees, and k-Nearest Neighbor) are applied to survey data collected in the province of Burgos (Spain). Available data suffer from a common problem in real-life datasets (data unbalance) as there are very few negative recommendations. In order to address such problem, that penalizes learning, data balancing techniques have been also applied. The satisfactory results validate the proposed application, being a useful tool for tourist managers.
PB World Scientific Publishing
SN 0219-6220
YR 2022
FD 2022-04
LK http://hdl.handle.net/10259/10020
UL http://hdl.handle.net/10259/10020
LA eng
NO This work was financially supported by the Society for the Development of the Province of Burgos thanks to the collaboration agreement signed between the University of Burgos and this institution for the development of actions included in PEBUR 15-20, in the fields of tourism, economic environment and human capital.
DS Repositorio Institucional de la Universidad de Burgos
RD 06-feb-2025