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User Interest Based Complex Web Information Visualization

  • Shibli Saleheen
  • Wei Lai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)

Abstract

Web graph allows end user to visualize web information and its connectivity. But due to its huge size, it is very challenging to visualize web information from the enormous cyberspace. As a result, web graph lacks simplicity while the user is searching for information. Clustering and filtering techniques have been employed to make the visualization concise but it is often not accurate to the expectation of user because they do not utilize user-centric information. To deliver the information concisely and precisely to the end users according to their interest, we introduce personalized clustering techniques for web information visualization. We propose a system architecture, which considers the user interests while clustering & filtering, to make the web information more meaningful and useful to the end users. By the implementation of the architecture, an experimental example is provided to reflect our approach.

Keywords

Clustering Personalization Visualization 

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References

  1. 1.
    Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, pp. 5–14 (2009)Google Scholar
  2. 2.
    Bennett, P.N., Radlinski, F., White, R.W., Yilmaz, E.: Inferring and using location metadata to personalize web search. In: SIGIR, pp. 135–144 (2011)Google Scholar
  3. 3.
    Card, S.: Information visualization. In: The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications (2007)Google Scholar
  4. 4.
    Gao, J., Lai, W.: Visualizing blogsphere using content based clusters. In: Web Intelligence and Intelligent Agent Technology, pp. 832–835 (2008)Google Scholar
  5. 5.
    Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Huang, X., Eades, P., Lai, W.: A framework of filtering, clustering and dynamic layout graphs for visualization. In: ACSC, pp. 87–96 (2005)Google Scholar
  7. 7.
    Keim, D.A., Mansmann, F., Schneidewind, J., Ziegler, H.: Challenges in visual data analysis. In: Information Visualization, pp. 9–16 (2006)Google Scholar
  8. 8.
    Lin, D.: An information-theoretic definition of similarity. In: ICML, pp. 296–304 (1998)Google Scholar
  9. 9.
    Matthijs, N., Radlinski, F.: Personalizing web search using long term browsing history. In: WSDM, pp. 25–34 (2011)Google Scholar
  10. 10.
    Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized search on the world wide web. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 195–230. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW, pp. 727–736 (2006)Google Scholar
  12. 12.
    Rattigan, M.J., Maier, M., Jensen, D.: Graph clustering with network structure indices. In: ICML, pp. 783–790 (2007)Google Scholar
  13. 13.
    Smyth, B., Balfe, E., Boydell, O., Bradley, K., Briggs, P., Coyle, M., Freyne, J.: A live-user evaluation of collaborative web search. In: IJCAI, pp. 1419–1424 (2005)Google Scholar
  14. 14.
    Wang, X., Zhai, C.: Learn from web search logs to organize search results. In: SIGIR, pp. 87–94 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shibli Saleheen
    • 1
  • Wei Lai
    • 1
  1. 1.Faculty of Information and Communication TechnologiesSwinburne University of TechnologyHawthornAustralia

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