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Experimental Verification of Sightseeing Information as a Weak Trigger to Affect Tourist Behavior

  • Yuuki Hiraishi
  • Takayoshi Kitamura
  • Tomoko Izumi
  • Yoshio Nakatani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10913)

Abstract

In this research, we verify information of sightseeing spots as a weak trigger which gives strolling tourists a chance to change their behaviors but does not specify the spot in a recommendation system. In a general recommendation system, the system provides complete piece of information about recommended spots. However, the provided information may deprive users of opportunities to discover interesting something by themselves. On the other hand, if no information is recommended to tourists, they may stroll in a restricted area because they have no hints of unfamiliar area. To reveal an appropriate information solving the above problems, we focus on the amount of information provided to users. Information about sightseeing spots is classified into the position and the feature information of a spot. For each information, we define the four categories of information according to the amount of information. We conducted the experiment with some subjects, and analyzed the impact on the information of these categories.

Keywords

User interface Nudge Suggestive methods Sightseeing support system Recommendation system 

References

  1. 1.
    Ishimori, S.: The potentialities of autonomous tourism in the twenty-first century. Senri Ethnol. Rep. 23, 5–14 (2001)Google Scholar
  2. 2.
    Lucchese, C., Perego, R., Silvestri, F., Vahabi, H., Venturini, R.: How random walks can help tourism. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 195–206. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28997-2_17CrossRefGoogle Scholar
  3. 3.
    Kawakami, H.: Towards a system design focusing on the utility of inconvenience. Hum. Interface Soc. Trans. 11(1), 125–134 (2009)MathSciNetGoogle Scholar
  4. 4.
    Hasebe, Y., Kawakami, H., Hiraoka, T., Nozaki, K.: Guidelines of system design for embodying benefits of inconvenience. SICE J. Control Meas. Syst. Integr. (JCMSI) 8(1), 2–6 (2015)CrossRefGoogle Scholar
  5. 5.
    Nakatani, Y., Ichikawa, K.: Tourist navigation system that induces accidental encounter. Hum. Interface Soc. Trans. 12(4), 439–449 (2010)Google Scholar
  6. 6.
    Tanaka, K., Nakatani, Y.: Sightseeing navigation system that promotes interaction with environment by restricting information. In: IEEE International Conference on System, Man, and Cybernetics (SMC), pp. 453–458 (2010)Google Scholar
  7. 7.
    Takagi, S., Izumi, T., Nakatani, Y.: Tour navigation system using landmarks that are customized by personal preference. In: The First International Symposium on Socially and Technically Symbiotic Systems (STSS), pp. 47-1–47-7 (2012)Google Scholar
  8. 8.
    Oku, K., Hattori, F., Kawagoe, K.: Tweet-mapping method for tourist spots based on now-tweets and spot-photos. Procedia Comput. Sci. 60, 1318–1327 (2015)CrossRefGoogle Scholar
  9. 9.
    Misu, T., Mizukami, E., Sugiura, K., Iwahashi, N.: Development of dialogue systems “‘Kyo-no Hanna’ and ‘Kyo no Osusume’”. J. Natl. Inst. Inf. Commun. Technol. 59(314), 29–33 (2012)Google Scholar
  10. 10.
    Matsumura, N., Fruchter, R., Leifer, L.: Shikakeology: designing triggers for behavior change. AI Soc. 30(4), 419–429 (2015)CrossRefGoogle Scholar
  11. 11.
    Yamane, S.: Shikake as a nudge. J. Artif. Intell. Soc. 28(4), 596–600 (2014)Google Scholar
  12. 12.
    Kurata, Y.: Potential-of-interest maps for mobile tourist information services. In: Fuchs, M., Ricci, F., Cantoni, L. (eds.) Information and Communication Technologies in Tourism, pp. 239–248. Springer, Vienna (2012).  https://doi.org/10.1007/978-3-7091-1142-0_21CrossRefGoogle Scholar
  13. 13.
    Izumi, T., Kitamura, T., Nakatani, Y.: A Suggestive recommendation method to make tourists “feel like going”. In: The 13th IFAC/IFIP/ IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, FriHTrack-31 (2016)CrossRefGoogle Scholar
  14. 14.
    Apple - Official website: www.apple.com/. Accessed 17 July 2017
  15. 15.
    Segaran, T., Toyama, Y., Kamosawa, M.: Collective Knowledge Programming. O’Reilly, Sebastopol (1991)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yuuki Hiraishi
    • 1
  • Takayoshi Kitamura
    • 1
  • Tomoko Izumi
    • 2
  • Yoshio Nakatani
    • 1
  1. 1.Ritsumeikan UniversityKusatsu SigaJapan
  2. 2.Osaka Institute of TechnologyHirakata OsakaJapan

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