Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

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

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

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

  2. http://protege.stanford.edu/.

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

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

References

  1. García-Crespo, A., Chamizo, J., Rivera, I., Mencke, M., Colomo-Palacios, R., & Gómez-Berbís, J. M. (2009). Speta: Social pervasive e-tourism advisor. Telematics and Informatics, 26(3), 306–315. doi:10.1016/j.tele.2008.11.008.

    Article  Google Scholar 

  2. García-Crespo, A., López-Cuadrado, A. R., Mencke, M., Berbís, J. M. G., & Palacios, R. C. (2010). Oddin: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements. Expert Systems with Applications, 37(3), 2621–2628. http://dblp.uni-trier.de/db/journals/eswa/eswa37.html#Garcia-CrespoGMBP10.

  3. García-Crespo, Á., Palacios, R. C., Berbís, J. M. G., Chamizo, J., & Rivera, I. (2010). Intelligent decision-support systems for e-tourism: Using speta ii as a knowledge management platform for dmos and e-tourism service providers. International Journal of Decision Support System Technology, 2(1), 36–48.

    Google Scholar 

  4. García-Crespo, A., López-Cuadrado, J. L., Colomo-Palacios, R., González-Carrasco, I., & Ruiz-Mezcua, B. (2011). Sem-fit: A semantic based expert system to provide recommendations in the tourism domain. Expert Systems with Applications, 38(10), 13310–13319. doi:10.1016/j.eswa.2011.04.152.

    Article  Google Scholar 

  5. Gong, R., Ning, K., Li, Q., O’Sullivan, D., Chen, Y., & Decker, S. (2009). Context modeling and measuring for proactive resource recommendation in business collaboration. Computers and Industrial Engineering, 57(1), 27–36. doi:10.1016/j.cie.2008.07.003.

    Article  Google Scholar 

  6. Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220. doi:10.1006/knac.1993.1008.

    Google Scholar 

  7. Kanellopoulos, D. N. (2008). An ontology-based system for intelligent matching of travellers’ needs for group package tours. International Journal of Digital Culture and Electronic Tourism, 1, 76–99.

    Article  Google Scholar 

  8. Lemire, D., & Maclachlan, A. (2005). Slope one predictors for online rating-based collaborative filtering. In Proceedings of SIAM data mining (SDM’05). http://www.daniel-lemire.com/fr/documents/publications/lemiremaclachlan_sdm05.pdf.

  9. Lenar, M., & Sobecki, J. (2005). Using recommendation to improve negotiations in agent-based systems. In Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems—Volume Part II, KES’05 (pp. 534–540). Springer, Berlin. doi:10.1007/11552451_72.

  10. Mu, X., & Chen, Y. (2011). Fuzzy semantic personalized recommendation system modeling. Application Research of Computers, 28(4), 1429–1433.

    Google Scholar 

  11. Niemann, M., Mochol, M., & Tolksdorf, R. (2008). Enhancing hotel search with semantic web technologies. Journal of Theoretical and Applied Electronic Commerce Research, 3(2), 82–96. http://dl.acm.org/citation.cfm?id=1414667.1414675.

    Google Scholar 

  12. Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989). Affect Grid: A single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57(3), 493–502. doi:10.1037/0022-3514.57.3.493.

    Article  Google Scholar 

  13. Vanesa Aciar, S., Serarols-Tarres, C., & De la Rosa i Esteva, J. L. (2007). Increasing effectiveness in e-commerce: recommendations applying intelligent agents. International Journal of Business and Systems Research, 1(1), 81–97.

    Article  Google Scholar 

  14. Wang, S. L., & Wu, C. Y. (2011). Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with Applications, 38(9), 10831–10838. doi:10.1016/j.eswa.2011.02.083.

  15. Xu, H., Wu, X., Li, X., & Yan, B. (2009). Comprison study of internet recommendation system. Journal of Software, 29(2), 350–362.

    Article  Google Scholar 

  16. Yuxia, H., & Ling, B. (2009). A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the internet. Expert Systems with Applications, 36(1), 933–943. doi:10.1016/j.eswa.2007.10.019.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Feiyu Lin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-013-1550-9

Keywords