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Design and Implementation of a Tour Planning System for Telematics Users

  • Junghoon Lee
  • Euiyoung Kang
  • Gyung-Leen Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4707)

Abstract

Aiming at providing an efficient tour schedule to tourists driving with a telematics device, this paper designs and implements an intelligent tour planning system based on the personalized tour recommender that may generate lots of destinations. To overcome the problem of long response time due to the computation of O(2 n ·n!) complexity solver, we used initial set reduction, distributed computing via MPI-based Linux cluster, and finally Lin-Kernighan heuristic. An user interface was also implemented on a portable device using the utility of embedded operating system. Performance measurement results exhibit that the tour schedule can not only be offered to the user within 5 seconds when the number of TPOIs is less than 22, but also find a schedule whose satisfaction degree is very close to the optimal value.

Keywords

Recommender System Feasible Schedule Satisfaction Degree Stay Time Tour Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Lee, J., Park, G., Kim, H., Yang, Y., Kim, P., Kim, S.: A telematics service system based on the Linux cluster. In: Shiyi (ed.) ICCS 2007. LNCS, vol. 4490, pp. 660–667. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Ponnada, M., Sharda, N.: A high level model for developing intelligent visual travel recommender systems, ENTER (2007)Google Scholar
  3. 3.
    Ricci, F., Werthner, H.: Case base querying for travel planning recommendation. Information Technology & Tourism 4, 215–226 (2002)Google Scholar
  4. 4.
    Maruyama, A., Shibata, N., Murata, Y., Yasumoto, K., Ito, M.: P-tour: A personal navigation system for tourism. In: Proc. of 11-th World Congress on ITS 2, pp. 18–21 (2004)Google Scholar
  5. 5.
    Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Annual conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  6. 6.
    Schubert, P., Koch, M.: The power of personalization: Customer collaboration and virtual communities. In: Proceedings of the Conference on AMCIS, pp. 1955-1965 (2002)Google Scholar
  7. 7.
    Nasraoui, O., Petenes, C.: An intelligent web recommendation engine based on fuzzy approximate reasoning. Proceeding of the IEEE International Conference on Fuzzy System 2, 1116–1121 (2003)Google Scholar
  8. 8.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems (1999)Google Scholar
  9. 9.
    Kang, E., Kim, H., Cho, J.: Personalization method for tourist point of interest (POI) recommendation. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4251, pp. 392–400. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Goldberg, A., Kaplan, H., Werneck, R.: Reach for A*: Efficient point-to-point shortest path algorithms. MSR-TR-2005-132. Microsoft (2005)Google Scholar
  12. 12.
    Pacheco, P.: Parallel Programming with MPI. Morgan Kaufmann Publishers, San Francisco (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Junghoon Lee
    • 1
  • Euiyoung Kang
    • 2
  • Gyung-Leen Park
    • 1
  1. 1.Dept. of Computer Science and Statistics, Cheju National University, 66 Jeju-daehakno, Jeju-City, Jeju-Do, 690-756Rep. of Korea
  2. 2.Dept. of Computer Education, Cheju National University, 66 Jeju-daehakno, Jeju-City, Jeju-Do, 690-756Rep. of Korea

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