http://www.cnr.it/ontology/cnr/individuo/prodotto/ID278620

LearNext: learning to predict tourists movements (Abstract/Comunicazione in atti di convegno)

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LearNext: learning to predict tourists movements (Abstract/Comunicazione in atti di convegno) (literal)

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Baraglia R., Muntean C. I., Nardini F.M., Silvestri F. (2014)
LearNext: learning to predict tourists movements
in 5th Italian Information Retrieval Workshop, University of Roma Tor Vergata, 21-22 January 2014
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CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Yahoo! Research, Barcelona, Spain; (literal)

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In this paper, we tackle the problem of predicting the \"next\" geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Rank- ing 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 touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions. (literal)

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