In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.

Where shall we go today? Planning touristic tours with tripbuilder

Nardini F M;Perego R;Renso C
2013

Abstract

In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.
2013
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
978-1-4503-2263-8
Tourist trip recommendation
Trajectory mining
H.3.3 Information Search and Retrieval
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/253209
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