Abstract
The present work deals with implementing tourist recommendation systems designed to predict the user preferences about a place or tourist activity in Mexico. Three recommendation systems have been proposed: two based on collaborative filtering (user and items) and the other based on demographic issues. To this aim, a corpus has been built by collecting 2,263 ratings from TripAdvisor.com about eighteen tourist places in Mexico. Experimental results show that the demographic-based recommendation system outperforms those based on collaborative filtering, obtaining a mean absolute error of 0.67 and a mean square error of 1.2980. These results also show significant improvement over a majority class baseline based on a sizeable unbalanced corpus.
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This work was partially supported by the Tecnológico Nacional de México (Grants No. 8288.20-P and 9518.20-P).
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Arce-Cardenas, S., Fajardo-Delgado, D., Álvarez-Carmona, M.Á., Ramírez-Silva, J.P. (2021). A Tourist Recommendation System: A Study Case in Mexico. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science(), vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_15
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