Multi-Agent Web Recommendations

  • Joaquim Neto
  • A. Jorge Morais
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 290)


Due to the large amount of pages in Websites it is important to collect knowledge about users’ previous visits in order to provide patterns that allow the customization of the Website. In previous work we proposed a multi-agent approach using agents with two different algorithms (associative rules and collaborative filtering) and showed the results of the offline tests. Both algorithms are incremental and work with binary data. In this paper we present the results of experiments held online. Results show that this multi-agent approach combining different algorithms is capable of improving user’s satisfaction.


Association Rule Recommender System MultiAgent System Collaborative Filter Autonomic Computing 
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.
    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.
    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
  3. 3.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems: an introduction. Cambridge University Press (2011)Google Scholar
  4. 4.
    Wooldridge, M.: An Introduction to MultiAgent Systems. John Wiley & Sons (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 ACM Symposium on Applied Computing, pp. 54–59. ACM Press, New York (2005)Google Scholar
  7. 7.
    Morais, A.J., Oliveira, E., Jorge, A.M.: A Multi-Agent Recommender System. In: Omatu, S., Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 281–288. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Perkowitz, M., Etzioni, O.: Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence 118(2000), 245–275 (2000)CrossRefzbMATHGoogle 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. Data Mining and Knowledge Discovery 6(1), 37–59 (2002)CrossRefMathSciNetGoogle 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. Data Mining and Knowledge Discovery 6(1), 61–82 (2002)CrossRefMathSciNetGoogle 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. Data Mining and Knowledge Discovery 7(4), 399–424 (2003)CrossRefMathSciNetGoogle 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 (2003)Google Scholar
  16. 16.
    Basilico, J., Hofmann, T.: Unifying collaborative and content-based filtering. In: Proceedings of ICML 2004, Twenty-first International Conference on Machine Learning. 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. Knowledge and Information Systems (KAIS) 5(4), 439–465 (2003)CrossRefGoogle Scholar
  18. 18.
    Lin, W., Alvarez, S.A., Ruiz, C.: Efficient Adaptive-Support Association Rule Mining for Recommender Systems. Data Mining and Knowledge Discovery 6, 83–105 (2002)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Spiliopoulou, M., Pohle, C.: Data mining for measuring and improving the success of web sites. Journal of Data Mining and Knowledge Discovery, Special Issue on E-commerce 5(1-2), 85–114 (2001)CrossRefzbMATHGoogle 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, Redmond (1996)Google Scholar
  22. 22.
    Spiliopoulou, M., Faulstich, L.C.: WUM: a tool for web utilization analysis. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 184–203. Springer, Heidelberg (1999)CrossRefGoogle 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)CrossRefGoogle 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: Proceedings of the 13th Portuguese Conference on Artificial Intelligence (EPIA 2007), pp. 487–499 (2007)Google Scholar
  27. 27.
    Miranda, C., Jorge, A.M.: Incremental Collaborative Filtering for Binary Ratings. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2008), vol. 01, pp. 389–392. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  28. 28.
    JADE (Java AgentDEvelopment Framework) Website, (access date: January 31, 2014)
  29. 29.
    Asynchronous JavascriptAnd XML (AJAX), Mozilla Developer Center, (access date: January 31, 2014)
  30. 30.
    Apache Derby Website, (access date: January 31, 2014)
  31. 31.
    1000 Palavras Fotografia, (access date: January 31, 2014)
  32. 32.
    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
  33. 33.
    Cleverdon, C., Kean, M.: Factors Determining the Performance of Indexing Systems. Aslib Cranfield Research Project. Cranfield, England (1968)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joaquim Neto
    • 3
    • 4
  • A. Jorge Morais
    • 1
    • 2
    • 4
  1. 1.Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.Laboratory of Artificial Intelligence and Decision Support (LIAAD – INESC TEC L. A.)PortoPortugal
  3. 3.National Laboratory of Civil Engineering (LNEC)LisbonPortugal
  4. 4.Universidade Aberta (Portuguese Open University)LisbonPortugal

Personalised recommendations