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Semantic Enrichment for Geospatial Information in a Tourism Recommender System
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Author(s): Joan de la Flor (Science and Technology Park for Tourism and Leisure, Spain), Joan Borràs (Science and Technology Park for Tourism and Leisure, Spain), David Isern (Universitat Rovira i Virgili, Spain), Aida Valls (Universitat Rovira i Virgili, Spain), Antonio Moreno (Universitat Rovira i Virgili, Spain), Antonio Russo (Universitat Rovira i Virgili, Spain), Yolanda Pérez (Universitat Rovira i Virgili, Spain)and Salvador Anton-Clavé (Universitat Rovira i Virgili, Spain)
Copyright: 2013
Pages: 22
Source title:
Geographic Information Systems: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2038-4.ch130
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Abstract
Geospatial information is commonly used in tourism to facilitate activity planning, especially in a context of limited information on the territory, as it is common in the case of complex and heterogeneous tourism destination regions where the constrained spatial activity of visitor is likely to generate inefficiencies in the use of assets and resources, and hinder visitor satisfaction. Because of the large amount of spatial and non-spatial data associated with different resources and activities, it is a logical choice to use geographic information systems (GIS) for storing, managing, analyzing, and visualizing the data. Nevertheless, in order to facilitate personalized recommendations to visitors, interaction with Artificial Intelligence is needed. This chapter presents SigTur/E-Destination, a tourism recommender system based on a semantically-enriched GIS that provides regional tourist organizations and the industry with a new powerful tool for the sustainable management of their destinations. The recommendation system uses innovative Artificial Intelligence techniques, such as a hybrid method that integrates content-based and collaborative filtering and clustering methodologies that improve computational time.
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