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A content search system considering the activity and context of a mobile user

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Abstract

People routinely carry mobile devices in their daily lives and obtain a variety of information from the Internet in many different situations. In searching for information (content) with a mobile device, a user’s activity (e.g., moving or stationary) and context (e.g., commuting in the morning or going downtown in the evening) often change, and such changes can affect the user’s degree of concentration on his or her mobile device’s display and information needs. Therefore, a search system should provide the user with an amount of information suitable for the current activity and a type of information suitable for the current context. In this study, we present the design and implementation of a content search system that considers a mobile user’s activity and context, with the goal of reducing the user’s operation load for content search. The proposed system switches between two kinds of content search systems according to the user’s activity: the location-based content search system is activated when the user is stationary (e.g., standing and sitting), while a menu-based content search system is activated when the user is moving (e.g., walking). Both systems present information according to user context. The location-based system presents detailed information via menus and a map according to location-based categories. The menu-based system presents only a few options to enable users to get content easily. Through user experiments, we confirmed that participants could get desired information more easily with this system than with a commercial search system.

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Notes

  1. http://mmd.up-date.ne.jp/.

  2. http://dir.yahoo.com.

  3. http://www.google.com/places/.

  4. http://code.google.com/intl/ja/apis/ajaxsearch/.

References

  1. Abowd G, Atkeson C, Hong J, Long S, Kooper R, Pinkerton M (1997) Cyberguide: a mobile context-aware tour guide. ACM Wire Netw 3(5):421–433

    Article  Google Scholar 

  2. Church K, Smyth B (2009) Understationary the intent be-hind mobile information needs. In: Proceedings of IUI, pp 247–256

  3. Figo D, Diniz PC, Ferreira DR, Cardoso JMP (2010) Preprocessing techniques for context recognition from accelerometer data. J Pers Ubiquit Comput 14(7):645–662

    Article  Google Scholar 

  4. Gavalas D, Kenteris M (2011) A web-based pervasive recommendation system for mobile tourist guides. J Pers Ubiquit Comput 15(7):759–770

    Article  Google Scholar 

  5. Heimonen T, Kaki M (2007) Mobile findex—supporting mobile web search with automatic result category. In: Proceedings of MobileHCI, pp 397–404

  6. Kamvar M (2008) Query suggestions for mobile search: understationary usage patterns. In: Proceedings of CHI, pp 1013–1016

  7. Karlson AK, Robertson G, Robbins DC, Czerwinski M, Smith G (2006) FaThumb: a facet-based interface for mobile search. In: Proceedings of CHI, pp. 711–720

  8. Kawahara Y, Hayashi T, Tamura H, Morikawa H, Aoyama T (2004) A context-aware content delivery service using off-the-shelf sensors. In: Proceedings of Mobisys

  9. Kern N, Schiele B, Schmidt A (2007) Recognizing context for annotating a live life recording. J Pers Ubiquit Comput 11(4):251–263

    Article  Google Scholar 

  10. Komaki D, Hara T, Hattori G, Takishima Y, Nishio S (2010) Investigation on information needs and search behaviors in mobile environments. IEICE SIG-WI2 65–70. (in Japanese)

  11. Krumm J, Horvitz E (2006) Inferring destinations from partial trajectories. In: Proceedings of UbiComp, pp 243–260

  12. Liao L, Fox D, Kaultz H (2005) Location-based activity recognition using relational markov networks. In: Proceedings of IJCAI, pp 773–778

  13. Mashita T, Shimatani K, Iwata M, Miyamoto H, Komaki D, Hara T, Kiyokawa K, Takemura H, Nishio S (2012) Human activity recognition for a content search system considering situations of Smartphone users. In: Proceedings of AMBIT, pp 22–23

  14. Naganuma T, Kurakake S (2005) Task knowledge based retrieval for services relevant to mobile user’s activity. In: Proceedings of ISWC, pp 959–973

  15. Newcomb E, Pashley T, Stasko J (2003) Mobile computing in the retail arena. In: Proceedings of CHI, pp 337–344

  16. Sasajima M, Kitamura Y, Naganuma T, Kurakake S, Mizoguchi R (2007) Task ontology—based modeling framework for navigation of mobile internet services. In: Proceedings of EuroIMSA’07, pp 47–55

  17. Schildbach B, Rukzio E (2010) Investigating selection and reading performance on a mobile phone while movement. In: Proceedings of MobileHCI, pp 93–102

  18. Setten MV, Pokraev S, Koolwaaij J (2004) Context-aware recommendations in the mobile tourist application COMPASS, adaptive hypermedia and adaptive web-based systems, Springer, LNCS, pp 515–548

  19. Sohn T, Li KA, Griswold WG, Hollan JD (2008) A diary study of mobile information needs. In: Proceedings of CHI, pp 433–442

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Acknowledgment

We thank Dr. Yasuyuki Nakajima, President and CEO of KDDI R&D Laboratories Inc., for his continuous support for this study. This research was partially supported by the Global Center of Excellence Program of the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, Japan

    Mayu Iwata, Hiroki Miyamoto, Takahiro Hara, Daijiro Komaki, Kentaro Shimatani, Tomohiro Mashita, Kiyoshi Kiyokawa, Shojiro Nishio & Haruo Takemura

  2. KDDI R&D Laboratories Inc, 2-1-15 Ohara, Fujimino, Saitama, Japan

    Toshiaki Uemukai & Gen Hattori

Authors
  1. Mayu Iwata
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  2. Hiroki Miyamoto
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  3. Takahiro Hara
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  4. Daijiro Komaki
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  5. Kentaro Shimatani
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  6. Tomohiro Mashita
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  7. Kiyoshi Kiyokawa
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  8. Toshiaki Uemukai
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  9. Gen Hattori
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  10. Shojiro Nishio
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  11. Haruo Takemura
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Corresponding author

Correspondence to Mayu Iwata.

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Iwata, M., Miyamoto, H., Hara, T. et al. A content search system considering the activity and context of a mobile user. Pers Ubiquit Comput 17, 1035–1050 (2013). https://doi.org/10.1007/s00779-012-0550-1

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  • DOI: https://doi.org/10.1007/s00779-012-0550-1

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