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Abstract

Web growth has brought several problems to users. The large amount of information that exists nowadays in some particular Websites turns the task of finding useful information very difficult. Knowing users’ visiting pattern is crucial to owners, so that they may transform or customize the Website. This problem originated the concept known as Adaptive Website: a Website that adapts itself for the purpose of improving the user’s experience. This paper describes a proposal for a doctoral thesis. The main goal of this work is to follow a multi-agent approach for Web adaptation. The idea is that all knowledge administration about the Website and its users, and the use of that knowledge to adapt the site to fulfil user’s needs, are made by an autonomous intelligent agent society in a negotiation environment. The complexity of the problem and the inherently distributed nature of the Web, which is an open, heterogeneous and decentralized network, are reasons that justify the multi-agent approach. It is expected that this approach enables real-time Web adaptation with a good level of benefit to the users.

Keywords

Association Rule Recommender System MultiAgent System Negotiation Environment Navigation Pattern 
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.

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References

  1. 1.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Menlo Park (1996)Google Scholar
  2. 2.
    Etzioni, O.: The World Wide Web: Quagmire or gold mine? Communications of the ACM 39(11), 65–68 (1996)CrossRefGoogle Scholar
  3. 3.
    Cooley, R., Mobasher, B., Srivastava, J.: Web mining: Information and patterns discovery on the world wide Web. In: Proceedings of the ninth IEEE International Conference on Tools with Artificial Intelligence, Newport Beach, California, pp. 558–567 (1997)Google Scholar
  4. 4.
    Wooldridge, M.: An Introduction to MultiAgent Systems. John Wiley & Sons, Chichester (2002)Google Scholar
  5. 5.
    Ardissono, L., Goy, A., Petrone, G., Segnan, M.: A multi-agent infrastructure for developing personalized web-based systems. ACM Trans. Inter. Tech. 5(1), 47–69 (2005)CrossRefGoogle Scholar
  6. 6.
    Albayrak, S., Wollny, S., Varone, N., Lommatzsch, A., Milosevic, D.: Agent technology for personalized information filtering: the pia-system. In: SAC 2005: Proceedings of the 2005 ACM symposium on Applied computing, pp. 54–59. ACM Press, New York (2005)CrossRefGoogle Scholar
  7. 7.
    Wei, Y.Z.: A Market-Based Approach to Recommendation Systems, PhD thesis, University of Southampton (2005)Google Scholar
  8. 8.
    Perkowitz, M., Etzioni, O.: Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence 118(2000), 245–275 (2000)zbMATHCrossRefGoogle Scholar
  9. 9.
    Ishikawa, H., Ohta, M., Yokoyama, S., Nakayama, J., Katayama, K.: Web usage mining approaches to page recommendation and restructuring. International Journal of Intelligent Systems in Accounting, Finance & Management 11(3), 137–148 (2002)CrossRefGoogle Scholar
  10. 10.
    El-Ramly, M., Stroulia, E.: Analysis of Web-usage behavior for focused Web sites: a case study. Journal of Software Maintenance and Evolution: Research and Practice 16(1-2), 129–150 (2004)CrossRefGoogle Scholar
  11. 11.
    Berendt, B.: Using Site Semantics to Analyze, Visualize, and Support Navigation. In: Data Mining and Knowledge Discovery, vol. 6(1), pp. 37–59 (2002)Google Scholar
  12. 12.
    Borges, J.L.: A Data Mining Model to Capture User Web Navigation Patterns, PhD thesis, University College London, University of London (2000)Google Scholar
  13. 13.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. In: Data Mining and Knowledge Discovery, vol. 6(1), pp. 61–82. Kluwer Publishing, Dordrecht (2002)Google Scholar
  14. 14.
    Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. In: Data Mining and Knowledge Discovery, vol. 7(4), pp. 399–424 (2003)Google Scholar
  15. 15.
    Jorge, A., Alves, M.A., Grobelnik, M., Mladenic, D., Petrak, J.: Web Site Access Analysis for A National Statistical Agency. In: Mladenic, D., Lavrac, N., Bohanec, M., Moyle, S. (eds.) Data Mining And Decision Support: Integration And Collaboration. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  16. 16.
    Basilico, J., Hofmann, T.: Unifying collaborative and content-based filtering. In: Proceedings of Twenty-first International Conference on Machine Learning, ICML 2000. ACM Press, New York (2004)Google Scholar
  17. 17.
    Masseglia, F., Teisseire, M., Poncelet, P.: HDM: A client/server/engine architecture for real time web usage mining. In: Knowledge and Information Systems (KAIS), vol. 5(4), pp. 439–465 (2003)Google Scholar
  18. 18.
    Lin, W., Alvarez, S.A., Ruiz, C.: Efficient Adaptive-Support Association Rule Mining for Recommender Systems. In: Data Mining and Knowledge Discovery, vol. 6, pp. 83–105 (2002)Google Scholar
  19. 19.
    Spiliopoulou, M., Pohle, C.: Data mining for measuring and improving the success of web sites. In: Kohavi, R., Provost, F. (eds.) Journal of Data Mining and Knowledge Discovery, Special Issue on E-commerce, vol. 5(1-2), pp. 85–114. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  20. 20.
    Armstrong, R., Freitag, D., Joachims, T., Mitchell, T.: WebWatcher: A learning apprentice for the world wide web. In: Proceedings of the AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, California, pp. 6–12 (1995)Google Scholar
  21. 21.
    Fink, J., Kobsa, A., Nill, A.: User-oriented adaptivity and adaptability in the AVANTI project. In: Designing for the Web: Empirical Studies, Microsoft Usability Group, Redmond, Washington (1996)Google Scholar
  22. 22.
    Spiliopoulou, M., Faulstich, L.C.: WUM: a tool for web utilization analysis. In: Proceedings of the International Workshop on the Web and Databases, Valencia, Spain, pp. 184–203 (1998)Google Scholar
  23. 23.
    Masseglia, F., Teisseire, M., Poncelet, P.: Real Time Web Usage Mining: a Heuristic Based Distributed Miner. In: Second International Conference on Web Information Systems Engineering (WISE 2001), vol. 1, p. 0288 (2001)Google Scholar
  24. 24.
    Jennings, N.R.: An agent-based approach for building complex software systems. Communications of the ACM 44(4), 35–41 (2001)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Kephart, J.O.: Research challenges of autonomic computing. In: ICSE 2005: Proceedings of the 27th International Conference on Software Engineering, pp. 15–22. ACM Press, New York (2005)Google Scholar
  26. 26.
    Domingues, M.A., Jorge, A.M., Soares, C., Leal, J.P., Machado, P.: A data warehouse for web intelligence. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS, vol. 4874, pp. 487–499. Springer, Heidelberg (2007)Google Scholar
  27. 27.
    JADE (Java Agent DEvelopment Framework) (Website: access date: 01/11/2008), http://jade.tilab.com
  28. 28.
    Asynchronous Javascript And XML (AJAX), Mozilla Developer Center (access date: 01/11/2008), http://developer.mozilla.org/en/docs/ajax

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • A. Jorge Morais
    • 1
  1. 1.Lecturer Faculty of Engineering of the Univeristy of Porto, PhD Student Laboratory of Artificial Intelligence and Data Analysis (LIAAD – INESC Porto L. A.), PhD Student ResearcherUniversidade Aberta (Portuguese Open University) 

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