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. We introduce a 68-dimensional feature vector tailored to tourism data and compare the proposed methods against state-of-the-art competitors and popularity-based baselines, showing that the learning-based approaches outperform the alternatives.
LearNext: Learning to predict tourists movements / Baraglia, R., Muntean, C.I., Nardini, F.M., Silvestri, F.. - 1127:(2014), pp. 75-79. (5th Italian Information Retrieval Workshop (IIR 2014) Roma, Italy ).
LearNext: Learning to predict tourists movements
Nardini F. M.;Silvestri F.
2014
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. We introduce a 68-dimensional feature vector tailored to tourism data and compare the proposed methods against state-of-the-art competitors and popularity-based baselines, showing that the learning-based approaches outperform the alternatives.| File | Dimensione | Formato | |
|---|---|---|---|
|
Baraglia_LearNext_2014.pdf
accesso aperto
Note: https://ceur-ws.org/Vol-1127/paper10.pdf
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Creative commons
Dimensione
173.52 kB
Formato
Adobe PDF
|
173.52 kB | Adobe PDF |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
