PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Baraglia R., Muntean C. I., Nardini F. M., Silvestri F. LearNext: learning to predict tourists movements. In: IIR 2014 - 5th Italian Information Retrieval Workshop (University of Roma Tor Vergata, 21-22 January 2014). Abstract, pp. 75 - 79. Roberto Basili, Fabio Crestani, Marco Pennacchiotti (eds.). (CEUR Workshop Proceedings, vol. 1127). CEUR, 2014.
 
 
Abstract
(English)
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.
URL: http://ceur-ws.org/Vol-1127/paper10.pdf
Subject Learning to rank
Geographical PoI Prediction
H.3.3 Information Storage and Retrieval
Information systems


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