LearNext: learning to predict tourists movements (Contributo in atti di convegno)

Type
Label
  • LearNext: learning to predict tourists movements (Contributo in atti di convegno) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2505515.2505656 (literal)
Alternative label
  • Baraglia R., Muntean C. I., Nardini F. M., Silvestri F. (2013)
    LearNext: learning to predict tourists movements
    in CIKM '2013 - 22nd ACM International Conference on Information & Knowledge Management, San Francisco, USA, 27 October - 1 November 2013 2013
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Baraglia R., Muntean C. I., Nardini F. M., Silvestri F. (literal)
Pagina inizio
  • 751 (literal)
Pagina fine
  • 756 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?id=2505656&CFID=273625159&CFTOKEN=55241834 (literal)
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Yahoo! Research Barcelona, Spain. (literal)
Titolo
  • LearNext: learning to predict tourists movements (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-2263-8 (literal)
Abstract
  • 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 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 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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