A Meta-Level Architecture for Adaptive Applications

  • Fabrício J. Barth
  • Edson S. Gomi
Conference paper

Abstract

The goal of this work is to investigate meta-level architectures for adaptive systems. The main application area is the user modeling for mobile and digital television systems. The results of a set of experiments performed on the proposed architecture showed that it is possible to reuse the components responsible for user modeling if they are designed as meta-level components.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Palazzo, L.A.M. (2002) Sistemas de hipermídia adaptativa. In: JAI 2002-XXI Jornada de Atualizacão em Informática, http://gpia.ucpel.tche.br/~lpalazzo/sha/Google Scholar
  2. [2]
    Webb, G.I., Pazzani, M.J., Billsus, D. (2001) Machine learning for user modeling. User Modeling and User-Adapted Interaction 11: 19–29CrossRefGoogle Scholar
  3. [3]
    Danilowicz, C, Nguyen, H.C. (2002) Using user profiles in intelligent information retrieval. In Hacid, M.S., Rs, Z.W., Zighed, D.A., Kodratoff, Y., eds.: Foundations of Intelligent Systems. 13th International Symposium. Number LNAI 2366, Lyon, France, Springer-Verlag 223–231Google Scholar
  4. [4]
    Rich, E. (1999) Users are individuals: Individualizing user models. International Journal of Man-Machine Studies 51: 323–338Google Scholar
  5. [5]
    Papatheodorou, C. (2001) Machine learning in user modeling. In Paliouras, G., Karkaletsis, V., Spyropoulos, C.D., eds.: Machine Learning and Applications. Number LNAI 2049. Springer-Verlag Berlin Heidelberg, Berlin 286–294Google Scholar
  6. [6]
    Orwant, J. (1995) Heterogeneous learning in the doppelganger user modeling system. User Modeling and User-Adapted Interaction 4: 107–130CrossRefGoogle Scholar
  7. [7]
    Fink, J., Kobsa, A. (2002) User modeling for personalized city tours. Artificial Intelligence Review 18: 33–74CrossRefGoogle Scholar
  8. [8]
    Kobsa, A. (2001) Generic user modeling systems. User Modeling and User-Adapted Interaction 11:49–63MATHCrossRefGoogle Scholar
  9. [9]
    Pohl, W., Nick, A. (1999) Machine learning and knowledge representation in the labour approach to user modeling. Proceedings of the Seventh International Conference on User Modeling. 179–188Google Scholar
  10. [10]
    FERBER, J. (1989) Computational reflection in class based object-oriented languages. SIGPLAN Notices. 24: 317–326CrossRefGoogle Scholar
  11. [11]
    LISBÔA, M. (1997) Arquiteturas de meta-nível. Tutorial XI Simpósio Brasileiro de Engenharia de Software 1:210–298Google Scholar
  12. [12]
    WU, S. (1997) Reflective Java: making Java even more reflexible. http://www.ansa.co.uk, Cambridge, UK.Google Scholar
  13. [13]
    KICZALES, J.G.R., BODROW, D. (1991) The art of the metaobjects protocol. MIT Press, CambridgeGoogle Scholar
  14. [14]
    SUN (2004) Java Technology. http://java.sun.comGoogle Scholar
  15. [15]
    Linden, G., Smith, B., York, J.: (2003) Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Distributed Systems OnLine 1:24–26Google Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • Fabrício J. Barth
    • 1
  • Edson S. Gomi
    • 1
  1. 1.Laboratory of Knowledge Engineering (Knoma) Polytechnic SchoolThe University of Sao PauloBrazil

Personalised recommendations