Dynamically Personalized Web Service System to Mobile Devices

  • Sanggil Kang
  • Wonik Park
  • Young-Kuk Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4027)


We introduce a novel personalized web service system through mobile devices. By providing only users’ preferred web pages or smaller readable sections, service elements, the problem of the limitation of resource of mobile devices can be solved. In this paper, the preferred service elements are obtained from the statistical preference transactions among web pages for each web site. In computing the preference, we consider the ratio of the length of each web page and users’ staying time on it. Also, our system dynamically provides the personalized web service according to the different three cases such as the beginning stage, the positive feedback, and the negative feedback. In the experimental section, we demonstrate our personalized web service system and show how much the resource of mobile devices can be saved.


Mobile Device Bayesian Network Digital Library Service Region Collaborative Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jones, M., Marsden, G., Mohd-Nasir, N., Boone, K., Buchanan, G.: Improving Web Interaction on Small Displays. In: Proc. WWW8, vol. 1, pp. 51–59 (1999)Google Scholar
  2. 2.
    Palm, Inc., Web Clipping Development,
  3. 3.
    Fox, A., Goldberg, I., Gribble, S.D., Lee, D.C., Polito, A., Brewer, E.A.: A Experience With Top Gun Wingman: A Proxy-Based Graphical Web Browser for the 3Com PalmPilot. In: Conference Reports of Middleware (1998)Google Scholar
  4. 4.
    ProxiNet, Inc., ProxiWeb:
  5. 5.
    Buyukkokten, O., Garcia-Molina, H., Paepcke, A., Winograd, T.: Power Brower: Efficient Web Browsing for PDAs. In: Proc. CHI 2000, pp. 430–437 (2000)Google Scholar
  6. 6.
    Kim, J.W., Lee, B.H., Shaw, M.J., Chang, H.L., Nelson, M.: Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts. International Journal of Electronic Commerce 5(3), 45–62 (2001)Google Scholar
  7. 7.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)Google Scholar
  8. 8.
    Aggrawall, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. ACM SIGMOD Int’l. Conference on Management of Data, pp. 207–216 (1994)Google Scholar
  9. 9.
    Aggrawall, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. 20th Int’l Conference on Very Large Databases, pp. 478–499 (1994)Google Scholar
  10. 10.
    Ashrafi, M.Z., Tanizr, D., Smith, K.: ODAM: An Optimized Distributed Association Rule Mining algorithm. IEEE Distributed Systems Online 3(3), 1–18 (2004)CrossRefGoogle Scholar
  11. 11.
    Ciaramita, M., Johnson, M.: Explaining away ambiguity: Learning verb selectional preference with Bayesian networks. In: Proc. Intl. Conference on Computational Linguistics, pp. 187–193 (2000)Google Scholar
  12. 12.
    Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2001)MATHGoogle Scholar
  13. 13.
    Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Wordnet: An On-line Lexical Database. International Journal of Lexicography 3(4), 235–312 (1990)CrossRefGoogle Scholar
  14. 14.
    Lee, J.J.: Case-based plan recognition in computing domains. In: Proc. The Fifth International Conference on User Modeling, pp. 234–236 (1996)Google Scholar
  15. 15.
    Resnick, P., Lacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Internet Research Report, MIT Center for Coordination Scienc (1994),
  16. 16.
    Maltz, D.A.: Distributing Information for Collaborative Filtering on Usenet net News. SM Thesis, Massachusetts Institute of Technology, Cambridge, MA (1994)Google Scholar
  17. 17.
    Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: ACM Conference on Electronic Commerce, 158–166 (1999)Google Scholar
  18. 18.
    Linden, G., Smith, B., York, J.: Recommendations: Item-To-Item Collaborative Filtering. IEEE Internet Computing 7(1), 76–80 (2003)CrossRefGoogle Scholar
  19. 19.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  20. 20.
    Bollacker, K.D., Lawrence, S., Giles, C.L.: A System for Automatic Personalized Tracking of Scientific Literature on the Web. In: Proc. ACM Conference on Digital Libraries, pp. 105–113 (1999)Google Scholar
  21. 21.
    Torres, R., McNee, S.M., Abel, M., Konstan, J.A., Riedl, J.: Enhancing Digital Libraries with TechLens+. In: ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 228–236 (2004)Google Scholar
  22. 22.
    Cotter, P., Smyth, B.: Personalization Techniques for the Digital TV world. In: Proc. European Conference on Artificial Intelligence, pp. 701–705 (2000)Google Scholar
  23. 23.
    Lee, W.P., Yang, T.H.: Personalizing Information Appliances: A Multi-agent Framework for TV Program Recommendations. Expert Systems with Applications 25(3), 331–341 (2003)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Jeon, Y., Hwang, E.: Automatically Customizing Service Pages on the Web for Mobile Devices. In: Bianchi-Berthouze, N. (ed.) DNIS 2003. LNCS, vol. 2822, pp. 53–65. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  25. 25.
    Squid Web Proxy Cache,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sanggil Kang
    • 1
  • Wonik Park
    • 2
  • Young-Kuk Kim
    • 2
  1. 1.Department of Computer ScienceThe University of SuwonGyeonggi-doSouth Korea
  2. 2.Department of Computer EngineeringChungnam National UniveristyDaejeonSouth Korea

Personalised recommendations