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2013 | OriginalPaper | Chapter

Mobile Social Travel Recommender System

Authors : Ander Garcia, Isabel Torre, Maria Teresa Linaza

Published in: Information and Communication Technologies in Tourism 2014

Publisher: Springer International Publishing

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Abstract

Travel Recommender Systems (TRSs) help tourists discovering and selecting the Points of Interest (POIs) that best fit their preferences. Recommendations rely on the data available about the POIs of a destination, the knowledge about tourists and their preferences about categories, and recommendation algorithms. This paper presents a Mobile Social TRS. The recommendation process is divided in two independent processes: the generation of user models and the calculation of the recommended POIs. The recommender generates user models taking into account their explicit preferences about categories, demographic information, and the tags they have created. Then, similarities between users are based on the POIs they have rated. Finally, a hybrid filtering algorithm combines these models with a content-based and a collaborative filtering algorithm to calculate a list of recommended POIs. The recommender has been integrated in a mobile prototype of the CRUMBS social network and preliminary results of its partial validation are presented.
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Metadata
Title
Mobile Social Travel Recommender System
Authors
Ander Garcia
Isabel Torre
Maria Teresa Linaza
Copyright Year
2013
DOI
https://doi.org/10.1007/978-3-319-03973-2_1

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