User Profiling for Web Search Based on Biological Fluctuation

  • Yuki Arase
  • Takahiro Hara
  • Shojiro Nishio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5612)


Because of the information flood on the Web, it has become difficult to search necessary information. Although Web search engines assign authority values to Web pages and show ranked results, it is not enough to find information of interest easily, as users have to comb through reliable but out of the focus information. In this situation, personalization of Web search results is effective. To realize the personalization, a user profiling technique is essential, however, since the users’ interests are not stable and are versatile, it should be flexible and tolerant to change of the environment. In this paper, we propose a user profiling method based on the model of the organisms’ flexibility and environmental tolerance. We review the previous user profiling methods and discuss the adequacy of applying this model to user profiling.


User profile Web search biological fluctuation 


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  1. 1.
    Billsus, D., Pazzani, M.J.: A Personal News Agent that Talks, Learns and Explains. In: The Third Annual Conference on Autonomous Agents, Seattle, pp. 268–275 (1999)Google Scholar
  2. 2.
    Baraglia, R., Silvestri, F.: Dynamic Personalization of Web Sites Without User Intervention. Communication of the ACM 50(2), 63–67 (2007)CrossRefGoogle Scholar
  3. 3.
    Gasparetti, F., Micarelli, A.: Exploiting Web Browsing Histories to Identify User Needs. In: International Conference on Intelligent User Interfaces (IUI 2007), Hawaii, pp. 28–31 (2007)Google Scholar
  4. 4.
    Claypool, M., Le, P., Waseda, M., Brown, D.: Implicit Interest Indicators. In: The Sixth International Conference on Intelligent User Interfaces (IUI 2001), USA, pp. 33–40 (2001)Google Scholar
  5. 5.
    Kashiwagi, A., Urabe, I., Kaneko, K., Yomo, T.: Adaptive Response of a Gene Network to Environmental Changes by Fitness-Induced Attractor Selection. PLos ONE 1(1), e49 (2006)Google Scholar
  6. 6.
    Aarts, E., Korst, J.: Simulated Annealing and Boltzmann Machines. Wiley, New York (1989)zbMATHGoogle Scholar
  7. 7.
  8. 8.
    Leibnitz, K., Wakamiya, N., Murata, M.: Resilient Multi-Path Routing Based on a Biological Attractor-Selection Scheme. In: Ijspeert, A.J., Masuzawa, T., Kusumoto, S. (eds.) BioADIT 2006. LNCS, vol. 3853, pp. 48–63. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Leibnitz, K., Wakamiya, N., Murata, M.: Self-Adaptive Ad-Hoc/Sensor Network Routing with Attractor-Selection. In: IEEE GLOBECOM, San Francisco, pp. 1–5 (2006)Google Scholar
  10. 10.
    Kitajima, S., Hara, T., Terada, T., Nishio, S.: Filtering Order Adaptation Based on Attractor Selection for Data Broadcasting System. In: International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2009), Fukuoka (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yuki Arase
    • 1
  • Takahiro Hara
    • 1
  • Shojiro Nishio
    • 1
  1. 1.Department of Multimedia Engineering Graduate School of Information Science and TechnologyOsaka UniversityOsakaJapan

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