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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Acknowledgments
This paper is part of the CRUMBS project, contributed by 10 European organizations and co-funded by national resources under the Celtic-Plus scheme. The work presented in the paper has been financed within the Avanza program of the Ministry of Industry, Energy and Tourism of the Spanish Government (CRUMBS TSI-020400-2010-47).
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Garcia, A., Torre, I., Linaza, M.T. (2013). Mobile Social Travel Recommender System. In: Xiang, Z., Tussyadiah, I. (eds) Information and Communication Technologies in Tourism 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-03973-2_1
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DOI: https://doi.org/10.1007/978-3-319-03973-2_1
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