PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Muntean C. I., Nardini F. M., Silvestri F., Baraglia R. On learning prediction models for tourists paths. In: ACM Transactions on Intelligent Systems and Technology (TIST), vol. 7 (1) article n. 8. ACM, 2015. [Online First 1 October 2015]
 
 
Abstract
(English)
In this article, 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 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. Experiments show that the methods we propose consistently outperform the baselines and provide strong evidence of the performance and robustness of our solutions.
URL: http://dl.acm.org/citation.cfm?doid=2830012.2766459
DOI: 10.1145/2766459
Subject Algorithms
Design
Experimentation
H.3.3 Information Search and Retrieval


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