Developing an Adaptive Learning Based Tourism Information System Using Ant Colony Metaphor

  • Soumya Banerjee
  • Monica Chis
  • G. S. Dangayach
Part of the Studies in Computational Intelligence book series (SCI, volume 273)

Abstract

Automation in learning process is one of the major technical breakthroughs in machine learning paradigm. A substantial boost in adaptive learning has been initiated by simple steps of bio-inspired algorithm to learn the collective pattern of tourist service environment. This chapter is devoted on a live project implementation and testing of a learning model prototype in tourist information system and service industry. The elaborated model is followed by result sessions, which demonstrate that artificial agents could mimic the collective service and product pattern effectively compared to other contemporary techniques. The cost optimization to address the service issues in tourism industry could also be achieved with the help of such prototype models.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Palkoska, J., Pühretmair, F., Tjoa, A.M., Wagner, R., Wöß, W.: Advanced Query Mechanisms in Tourism Information Systems. In: Proceedings of the International Conference on Information and Communication Technologies in Tourism (ENTER 2002), pp. 438–447. Springer, Innsbruck (2002)Google Scholar
  2. 2.
    Bridge, D., Göker, M., McGinty, L., Smyth, B.: Case-based recommender systems. Knowledge Engineering Review 20(3), 315–320 (2006)CrossRefGoogle Scholar
  3. 3.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowledge and Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  4. 4.
    Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: An Environment for the Development of Knowledge-based Recommender Applications. International Journal of Electronic Commerce (2007)Google Scholar
  5. 5.
    Ponnada, M., Jakkilinki, R., Sharda, N.: Tourism recommender systems: Current technology and future directions. In: Pease, W., Rowe, M., Cooper, M. (eds.) Information and Communication Technologies in support of the tourism industry. Idea Group Inc., Hershey (2006)Google Scholar
  6. 6.
    Ponnada, M., Sharda, N.: A High level model for developing Intelligent Visual Travel Recommender Systems. In: Sigala, M., Mich, L., Murphy, J. (eds.) ENTER 2007: 14th annual conference of IFITT, the International Federation for IT & Travel and Tourism, Ljubljana, Slovenia, January 24-26. Springer, Vienna (2007)Google Scholar
  7. 7.
    Ponnada, M., Sharda, N.: A High level model for developing Intelligent Visual Travel Recommender Systems. In: Sigala, M., Mich, L., Murphy, J. (eds.) ENTER 2007: 14th annual conference of IFITT, the International Federation for IT & Travel and Tourism, Ljubljana, Slovenia, January 24-26. Springer, Vienna (2007)Google Scholar
  8. 8.
    Chen, J.N., Huang, Y.M., Chu, W.C.: Applying Dynamic Fuzzy Petri Net to Web Learning System. Interactive Learning Environments 13(3), 159–178 (2005)CrossRefGoogle Scholar
  9. 9.
    Huang, Y.M., Chen, J.N., Kuo, Y., Jeng, Y.L.: An intelligent human-expert forum System based on Fuzzy Information Retrieval Technique. Expert Systems with Applications 34(2) (2007)Google Scholar
  10. 10.
    Semet, Y., Lutton, E., Collet, P.: Ant colony optimization for e-learning: Observing the emergence of pedagogic suggestions. In: IEEE Swarm Intelligence Symposium, pp. 46–52 (2003)Google Scholar
  11. 11.
    Dorigo, M., Birattari, M., Stiitzle, T.: Ant colony optimization: Artificial Ants as a Computational Intelligence Technique. IEEE Computational Intelligence Magazine 1(4) (2006)Google Scholar
  12. 12.
    Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.): ANTS 2006. LNCS, vol. 4150. Springer, Heidelberg (2006)Google Scholar
  13. 13.
    Bekele, R.: Computer Assisted Learner Group Formation Based on Personality Traits. Ph.D Dissertation, University of Hamburg, Hamburg, Germany (2005), http://www.sub.unihamburg.de/opus/volltexte/2006/2759 (Retrieved February 10, 2009)
  14. 14.
    Doerr, B., Neumann, F., Sudholt, D., Witt, C.: On the runtime analysis of the 1-ANT ACO algorithm. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 33–40. ACM, New York (2007)CrossRefGoogle Scholar
  15. 15.
    Venkataiah, S., Sharda, N., Ponnada, M.: A Comparative Study of Continuous and Discrete Visualization of Tourism Information. In: Proceedings of the International Conference on Information and Communication Technologies in Tourism, ENTER 2008, Innsbruck, Austria, January 23-25 (2008)Google Scholar
  16. 16.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Syst. 23(1), 103–145 (2005)CrossRefGoogle Scholar
  17. 17.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inform. Retr. 4(2), 133–151 (2001)MATHCrossRefGoogle Scholar
  18. 18.
    Ricci, F., Del Missier, F.: Supporting Travel Decision Making through Personalized Recommendation. In: Karat, C.M., Blom, J., Karat, J. (eds.) Designing Personalized User Experiences for E-Commerce, pp. 221–251. Kluwer Academic Publisher, Dordrecht (2004)Google Scholar
  19. 19.
    Xiang, Z., Fesenmaier, D.: An analysis of two search engine interface metaphors for trip planning. Information Technology & Tourism 7(2), 103–117 (2005)CrossRefGoogle Scholar
  20. 20.
    Ricci, F.: Travel recommender Systems. IEEE Intelligent Systems, 55–57 (November/December 2002)Google Scholar
  21. 21.
    Ricci, F., Nguyen, Q.N.: Critique-Based Mobile Recommender Systems. OEGAI Journal 24(4) (2005)Google Scholar
  22. 22.
    Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: INTRIGUE: personalized recommendation of tourist attractions for desktop and handset devices. Applied AI, Special Issue on Artificial Intelligence for Cultural Heritage and Digital Libraries 17(8-9), 687–714 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Soumya Banerjee
    • 1
  • Monica Chis
    • 2
  • G. S. Dangayach
    • 3
  1. 1.Department of Computer Science and EngineeringBirla Institute of Technology, International CenterMauritius
  2. 2.Siemens Program and System Engineering (Siemens PSE)Romania
  3. 3.Department of Mechanical EngineeringMalviya National Institute of TechnologyJaipurIndia

Personalised recommendations