Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
We can not obtain enough posts in the early and mid-2000s because Panoramio is a relatively-new web service. Therefore, we did not compare half-yearly periods of them.
- 9.
Note that we did not analyze any text information for the time sequence feature. It is just the number of postings in each period.
References
Kawamura, H., Suzuki, K., Yamamoto, M., Matsubara, H.: Tourism informatics (special feature: New informatics). IPSJ Mag. 51(6), 642–648 (2010)
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)
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)
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)
Kanazawa, Y., Hidaka, Y., Ogawa, K.: Destination retrieval system using an association retrieval method. Int. J. Future. Comput. Commun. 2(3), 169–173 (2013)
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)
Kurata, Y.: Interactive assistance for tour planning. In: Spatial Cognition 2010. Lecture Notes in Artificial Intelligence, vol. 6222, pp. 289–302 (2010)
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)
Shimada, K., Uehara, H., Endo, T.: Sightseeing location recommendation system based on collective intelligence. Soc. Tour. Inform. 10 (2014)
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)
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)
Zhang, X., Shasha, D.: Better burst detection. In: Proceedings of the 22nd International Conference on Data Engineering (2006)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Shimada, K., Uehara, H., Endo, T. (2015). The Effectiveness of Time Sequence Information on a Sightseeing Spot Recommender. In: Matsuo, T., Hashimoto, K., Iwamoto, H. (eds) Tourism Informatics. Intelligent Systems Reference Library, vol 90. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47227-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-662-47227-9_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47226-2
Online ISBN: 978-3-662-47227-9
eBook Packages: EngineeringEngineering (R0)