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-theart 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.

On Learning Prediction Models for Tourists Paths / Muntean, Ci; Nardini, Fm; Silvestri, F; Baraglia, R. - In: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY. - ISSN 2157-6904. - 7:1(2015). [10.1145/2766459]

On Learning Prediction Models for Tourists Paths

Silvestri F
;
2015

Abstract

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-theart 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.
2015
Geographical PoI prediction; Learning to rank
01 Pubblicazione su rivista::01a Articolo in rivista
On Learning Prediction Models for Tourists Paths / Muntean, Ci; Nardini, Fm; Silvestri, F; Baraglia, R. - In: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY. - ISSN 2157-6904. - 7:1(2015). [10.1145/2766459]
File allegati a questo prodotto
File Dimensione Formato  
Muntean_On-learning_2015.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 526.06 kB
Formato Adobe PDF
526.06 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1476514
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 33
  • ???jsp.display-item.citation.isi??? 23
social impact