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Intell. Syst. Technol."],"published-print":{"date-parts":[[2015,10,16]]},"abstract":"<jats:p>In this article, we tackle the problem of predicting the \u201cnext\u201d geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist\u2019s current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68-dimension feature vector specifically designed for tourism-related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in recommender and trail prediction systems for tourism, as well as a popularity baseline. 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