The Effectiveness of Time Sequence Information on a Sightseeing Spot Recommender

  • Kazutaka Shimada
  • Hisashi Uehara
  • Tsutomu Endo
Part of the Intelligent Systems Reference Library book series (ISRL, volume 90)


We have already proposed a sightseeing spot recommendation system based on information on the Web. An input for the prototype system was a user’s favorite location or facility. Our system computed a similarity measure between a target location that a user selects and each sightseeing spot in our database. One interesting feature for the similarity calculation in our system is time sequence information of each sightseeing spot. The prototype system used the number of hits in Yahoo Chiebukuro for the feature. We regard the time sequence as the potential-of-interest days for each sightseeing spot. In this paper, we focus another information resource for the time sequence feature; Panoramio. Panoramio is a geolocation-oriented photo sharing website and is useful to obtain the time sequence feature. Chiebukuro and Panoramio have different characteristics. Therefore, we compare the two information resources. We discuss the overall difference, the burst points and visualization. We also discuss several aspects of the time sequence which includes the merit and demerit of the feature.


Sightseeing spot recommendation Time sequence Potential-of-interest days Yahoo Chiebukuro Panoramio 


  1. 1.
    Kawamura, H., Suzuki, K., Yamamoto, M., Matsubara, H.: Tourism informatics (special feature: New informatics). IPSJ Mag. 51(6), 642–648 (2010)Google Scholar
  2. 2.
    Saito, H.: Analysis of tourism informatics on web (special issue: Tourism informatics and artificial intelligence). J. Jpn. Soc. Artif. Intell. 26(3), 234–239 (2011)Google Scholar
  3. 3.
    Kurashima, T., Tezuka, T., Tanaka, K.: Blog map of experiences: Extracting and geographically mapping visitor experiences from city blogs. Lecture Notes in Computer Science, vol. 3806, pp. 496–503. Springer (2005)Google Scholar
  4. 4.
    Kurashima, T., Tezuka, T., Tanaka, K.: Mining and visualization of visitor experiences from urban blogs. In: Proceedings of the 17th International Conference on Database and Expert Systems Applications (DEXA 2006) (2006)Google Scholar
  5. 5.
    Kanazawa, Y., Hidaka, Y., Ogawa, K.: Destination retrieval system using an association retrieval method. Int. J. Future. Comput. Commun. 2(3), 169–173 (2013)Google Scholar
  6. 6.
    Kurata, Y., Hara, T.: Ct-planner4: toward a more user-friendly interactive day-tour planner. In: ENTER 2014 (Information and Communication Technologies in Tourism 2014), pp.73–86 (2014)Google Scholar
  7. 7.
    Kurata, Y.: Interactive assistance for tour planning. In: Spatial Cognition 2010. Lecture Notes in Artificial Intelligence, vol. 6222, pp. 289–302 (2010)Google Scholar
  8. 8.
    Okuyama, K., Yanai, K.: A travel planning system based on travel trajectories extracted from a large number of geotagged photos on the web. In: Proceedings of Pacific-Rim Conference on Multimedia (2011)Google Scholar
  9. 9.
    Shimada, K., Uehara, H., Endo, T.: Sightseeing location recommendation system based on collective intelligence. Soc. Tour. Inform. 10 (2014)Google Scholar
  10. 10.
    Kurata, Y.: Potential-of-interest maps for mobile tourist information services. In: ENTER 2012 (Information and Communication Technologies in Tourism 2012), pp.239–248 (2012)Google Scholar
  11. 11.
    Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2002), pp. 91–101 (2002)Google Scholar
  12. 12.
    Zhang, X., Shasha, D.: Better burst detection. In: Proceedings of the 22nd International Conference on Data Engineering (2006)Google Scholar
  13. 13.
    Zhu, Y., Shasha, D.: Efficient elastic burst detection in data streams. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’03), PP.336–345 (2003)Google Scholar
  14. 14.
    Shimada, K., Inoue, S., Endo, T.: On-site likelihood identification of tweets for tourism information analysis. In: Proceedings of 3rd IIAI International Conference on e-Services and Knowledge Management (IIAI ESKM 2012) (2012)Google Scholar
  15. 15.
    Shimada, K., Inoue, S., Maeda, H., End, T.: Analyzing tourism information on Twitter for a local city. In: Proceedings of SSNE2011, International Workshop on Innovative Tourism (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Kazutaka Shimada
    • 1
  • Hisashi Uehara
    • 1
  • Tsutomu Endo
    • 1
  1. 1.Kyushu Institute of TechnologyIizuka FukuokaJapan

Personalised recommendations