Machine Learning to Predict Recommendation by Tourists in a Spanish Province

Santiago Aparicio Castillo (), Nuño Basurto Hornillos (), Pablo Arranz Val (), Paula Antón Maraña () and à lvaro Herrero Cosío
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Santiago Aparicio Castillo: Department of Applied Economics, Faculty of Economics and Business Studies, University of Burgos, Pza. de la Infanta Da. Elena s/n, 09001 Burgos, Spain
Nuño Basurto Hornillos: ��Department of Computer Engineering, Polytechnic School, University of Burgos, Avda. Cantabria s/n, 09006 Burgos, Spain
Pablo Arranz Val: Department of Applied Economics, Faculty of Economics and Business Studies, University of Burgos, Pza. de la Infanta Da. Elena s/n, 09001 Burgos, Spain
Paula Antón Maraña: Department of Applied Economics, Faculty of Economics and Business Studies, University of Burgos, Pza. de la Infanta Da. Elena s/n, 09001 Burgos, Spain
à lvaro Herrero Cosío: ��Department of Computer Engineering, Polytechnic School, University of Burgos, Avda. Cantabria s/n, 09006 Burgos, Spain

International Journal of Information Technology & Decision Making (IJITDM), 2022, vol. 21, issue 04, 1297-1320

Abstract: 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.

Keywords: Artificial intelligence; supervised learning; classification; tourism management; recommendation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S021962202250016X

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