2026-09-18T08:16:38Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/100202025-01-24T01:05:32Zcom_10259_4386com_10259_5086com_10259_2604col_10259_4387

Aparicio Castillo, Santiago Basurto Hornillos, Nuño Arranz Val, Pablo Antón Maraña, Paula Herrero Cosío, Álvaro 2025-01-23T12:42:55Z 2025-01-23T12:42:55Z 2022-04 0219-6220 http://hdl.handle.net/10259/10020 10.1142/S021962202250016X 1793-6845 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. eng info:eu-repo/semantics/openAccess Artificial intelligence Supervised learning Classification Tourism management Recommendation Machine Learning to Predict Recommendation by Tourists in a Spanish Province info:eu-repo/semantics/article