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Context-based Ontology-driven Recommendation Strategies for Tourism in Ubiquitous Computing

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Abstract

Tourism is an information-intensive business. At present, there are a lot of information and tourism resources available on the internet that lead to low searching efficiency and effectiveness, the user may get too many seeking results but not related to his interest, or few results than his expected. The user can know clearly what he wants, but sometime the user doesn’t know what kind information he needs. User’s demand can be formulated as direct demand and potential preference. At the same time, the study shows that there is strong relationship between the traveler’s potential preference and the characteristics of tourism resources. In order to solve the information overload challenge, recommendation services are increasingly emerging. Currently, recommendation methods focus on dealing with personalized matching based on the user preference. However, these methods skip the user’s direct demand. In this paper, we propose ontology-driven recommendation strategies based on user’s context. The strategies use ontology to describe and integrate tourism resources, achieve the goal of associating user’s direct needs and his potential preference as the context in recommendation. Moreover, theoretical analysis and experiments show that the proposed approach is feasible, the results of the evaluation are discussed.

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Notes

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    http://www.w3.org/TR/owl-guide

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    http://protege.stanford.edu/.

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    http://incubator.apache.org/jena/about_jena/about.html.

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    http://www.w3.org/TR/rdf-sparql-query/.

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Correspondence to Feiyu Lin.

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Shi, L., Lin, F., Yang, T. et al. Context-based Ontology-driven Recommendation Strategies for Tourism in Ubiquitous Computing. Wireless Pers Commun 76, 731–745 (2014). https://doi.org/10.1007/s11277-013-1550-9

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Keywords

  • Context
  • Ontology
  • Recommendation
  • Tourism
  • Ubiquitous computing