A Tourist Spot Search System Based on Paragraph Vector Model of Location and Category Tags Using User Reviews

  • Daisuke Kitayama
  • Tomofumi Yoshida
  • Shinsuke Nakajima
  • Kazutoshi Sumiya
Conference paper

Abstract

Tourist spots have certain functions, such as “spot suitable for seeing the night view” or “spot suitable for meeting point”. In this paper, we propose a method for searching tourist spots that uses the similarity of their functional features. In our method, we extract distributed representations of a tourist spot as a paragraph vector of user reviews for the tourist spot. We extract distributed representations of location and category in the same way. Next, we extract the functional feature of a tourist spot by combining distributed representations of the tourist spot, locations, and categories. Finally, we search tourist spots using the extracted functional feature and distributed representations of other locations and categories. In this phase, the most important thing is the way in which locations and categories are represented. We can employ an extraction method using a paragraph vector for user reviews based on a location or a category (the conventional method). On the other hand, we can use the average vector of distributed representations of all spots in a location or a category. In this paper, we compared our method (average vector) with the conventional method (paragraph vector). By this experiment, we evaluated the effectiveness of our method for searching tourist spots.

Keywords

Geographical information retrieval Location based social network Paragraph vector model Text processing Tourist information User reviews 

Notes

Acknowledgements

This work was supported by MEXT KAKENHI Grant Number JP15K16091

References

  1. 1.
    One of the most famous travel website around the world. www.tripadvisor.com
  2. 2.
    One of the largest travel website in Japan. www.jalan.net
  3. 3.
    T. Yoshida, D. Kitayama, S. Nakajima, K. Sumiya, A tourist spot search method using similarity of function based on distributed representations of user reviews, in Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2017, 15–17 March, 2017, Hong Kong, pp. 473–478Google Scholar
  4. 4.
    Q.V. Le, T. Mikolov, Distributed representations of sentences and documents,” in Proceedings of the 31th International Conference on Machine Learning, ICML 2014 (2014), pp. 1188–1196Google Scholar
  5. 5.
    T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013)Google Scholar
  6. 6.
    Implementation of the algorithm proposed by Mikolov et al. https://code.google.com/archive/p/word2vec
  7. 7.
    Z.S. Harris, Distributional structure. Word 10(2–3), 146–162 (1954)CrossRefGoogle Scholar
  8. 8.
    R. Řehůřek, P. Sojka, Software Framework for Topic Modelling with Large Corpora, in Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (2010), pp. 45–50. http://is.muni.cz/publication/884893/en
  9. 9.
  10. 10.
    D.J. Crandall, L. Backstrom, D. Huttenlocher, J. Kleinberg, Mapping the world’s photos, in Proceedings of the 18th International Conference on World Wide Web (2009), pp. 761–770Google Scholar
  11. 11.
    M. Hirota, M. Shirai, H. Ishikawa, S. Yokoyama, “Detecting relations of hotspots using geo-tagged photographs in social media sites,” in Proceedings of Workshop on Managing and Mining Enriched Geo-Spatial Data (2014), pp. 7:1–7:6Google Scholar
  12. 12.
    K. Oku, F. Hattori, Mapping geotagged tweets to tourist spots considering activity region of spot, in Tourism Informatics, vol. 90 (Springer, Heidelberg, 2015), pp. 15–30Google Scholar
  13. 13.
    K. Fujii, H. Nanba, T. Takezawa, A. Ishino, Enriching travel guidebooks with travel blog entries and archives of answered questions, in Information and Communication Technologies in Tourism, Proceedings of the International Conference in Bilbao, Spain, 2–5 February 2016, ed. by A. Inversini, R. Schegg, vol. 2016 (Springer International Publishing, 2016) pp. 157–171Google Scholar
  14. 14.
    S. Nakajima, K. Tanaka, Relative queries and the relative cluster-mapping method. in Proceedings of the Database Systems for Advanced Applications: 9th International Conference, DASFAA 2004, Jeju Island, Korea, 17–19 March 2003, ed. by Y. Lee, J. Li, K.-Y. Whang, D. Lee. (Springer, Heidelberg, 2004), pp. 843–856Google Scholar
  15. 15.
    M.P. Kato, H. Ohshima, S. Oyama, K. Tanaka, Query by analogical example: Relational search using web search engine indices, in Proceedings of the 18th ACM Conference on Information and Knowledge Management (2009), pp. 27–36Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Daisuke Kitayama
    • 1
  • Tomofumi Yoshida
    • 1
    • 2
  • Shinsuke Nakajima
    • 3
  • Kazutoshi Sumiya
    • 4
  1. 1.Kogakuin UniversityTokyoJapan
  2. 2.Datalinks CorporationNew YorkUSA
  3. 3.Kyoto Sangyo UniversityKyotoJapan
  4. 4.Kwansei Gakuin UniversityHyōgoJapan

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