2026-05-11T16:25:46Zhttps://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 f7a24646-3802-4c9e-8b35-07e4ab23d8f5 500 Basurto Hornillos, Nuño 742 500 0000-0001-7289-4689 Arranz Val, Pablo 41 500 0000-0002-2608-9248 Antón Maraña, Paula 887 500 0000-0003-1299-1385 Herrero Cosío, Álvaro 283 500 0000-0002-2444-5384 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. 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. application/pdf eng World Scientific Publishing International Journal of Information Technology & Decision Making. 2022, V. 21, n. 4, p. 1297-1320 https://doi.org/10.1142/S021962202250016X Artificial intelligence Supervised learning Classification Tourism management Recommendation Turismo Economía Tourism Economics Machine Learning to Predict Recommendation by Tourists in a Spanish Province info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess International Journal of Information Technology & Decision Making 21 4 1297 1320 THUMBNAIL Aparicio-ijoitadm_2022.pdf.jpg Aparicio-ijoitadm_2022.pdf.jpg IM Thumbnail image/jpeg 3886 https://riubu.ubu.es/bitstream/10259/10020/3/Aparicio-ijoitadm_2022.pdf.jpg 3061d37f1f23a025cb6f14f3826e6c9a MD5 3 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/10020/2/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 2 ORIGINAL Aparicio-ijoitadm_2022.pdf Aparicio-ijoitadm_2022.pdf application/pdf 417591 https://riubu.ubu.es/bitstream/10259/10020/1/Aparicio-ijoitadm_2022.pdf f8b682cb7f287915c077e30d686c15ac MD5 1 10259/10020 oai:riubu.ubu.es:10259/10020 2025-01-24 02:05:32.762 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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