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Mobile Social Travel Recommender System

In: Information and Communication Technologies in Tourism 2014

Author

Listed:
  • Ander Garcia

    (Department of eTourism and Cultural Heritage)

  • Isabel Torre

    (Department of eTourism and Cultural Heritage)

  • Maria Teresa Linaza

    (Department of eTourism and Cultural Heritage)

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.

Suggested Citation

  • Ander Garcia & Isabel Torre & Maria Teresa Linaza, 2013. "Mobile Social Travel Recommender System," Springer Books, in: Zheng Xiang & Iis Tussyadiah (ed.), Information and Communication Technologies in Tourism 2014, edition 127, pages 3-16, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-03973-2_1
    DOI: 10.1007/978-3-319-03973-2_1
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    Cited by:

    1. Aleksander Groth & Daniel Haslwanter, 2016. "Efficiency, effectiveness, and satisfaction of responsive mobile tourism websites: a mobile usability study," Information Technology & Tourism, Springer, vol. 16(2), pages 201-228, June.

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