Advertisement

A Multiagent Recommender System with Task-Based Agent Specialization

  • Fabiana Lorenzi
  • Fabio Arreguy Camargo Correa
  • Ana L. C. Bazzan
  • Mara Abel
  • Francesco Ricci
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 44)

Abstract

This paper describes a multiagent recommender system where agents maintain local knowledge bases and, when requested to support a travel planning task, they collaborate exchanging information stored in their local bases. A request for a travel recommendation is decomposed by the system into sub tasks, corresponding to travel services. Agents select tasks autonomously, and accomplish them with the help of the knowledge derived from previous solutions. In the proposed architecture, agents become experts in some task types, and this makes the recommendation generation more efficient. In this paper, we validate the model via simulations where agents collaborate to recommend a travel package to the user. The experiments show that specialization is useful hence providing a validation of the proposed model.

Keywords

Recommender System Task Type Travel Agent Recommendation Process High Average Evaluation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Balabanovic, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the Association for Computing Machinery 40(3), 66–72 (1997)Google Scholar
  3. 3.
    Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE. Wiley, Chichester (2007)CrossRefGoogle Scholar
  4. 4.
    Billsus, D., Pazzani, M.: A hybrid user model for news story classification. In: Proceedings of the Seventh International Conference on User Modeling, UM 1999, Banff, Canada (1999)Google Scholar
  5. 5.
    Goy, A., Ardissono, L., Petrone, G.: Personalization in e-commerce applications. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 485–520. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Lorenzi, F., Ricci, F.: Case-based recommender systems: a unifying view. In: Mobasher, B., Anand, S. (eds.) Intelligent Techniques for Web Personalization, pp. 89–113. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Lorenzi, F., Santos, D.S., de Oliveira, D., Bazzan, A.L.C.: Task allocation in case-based recommender systems: a swarm intelligence approach. In: Lin, H. (ed.) Architectural Design of Multi-Agent Systems. Information Science Reference, pp. 268–279 (2007)Google Scholar
  8. 8.
    Macho, S., Torrens, M., Faltings, B.: A multi-agent recommender system for planning meetings. In: Workshop on Agent-based recommender systems (2000)Google Scholar
  9. 9.
    Maes, P.: Agents that reduce work and information overload. Commun. ACM 37(7), 30–40 (1994)CrossRefGoogle Scholar
  10. 10.
    Montaner, M., López, B., de la Rosa, J.L.: A taxonomy of recommender agents on the internet. Artificial Intelligence Review 19(4), 285–330 (2003)CrossRefGoogle Scholar
  11. 11.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings ACM Conference on Computer-Supported Cooperative Work, pp. 175–186 (1994)Google Scholar
  12. 12.
    Ricci, F.: Travel recommender systems. IEEE Intelligent Systems 17(6), 55–57 (2002)MathSciNetGoogle Scholar
  13. 13.
    Schafer, J., Konstan, J., Riedl, J.: E-commerce recommendation applications. Data Mining and Knowledge Discovery 5(1/2), 115–153 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Smyth, B.: Case-based recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Sycara, K.: Multiagents systems. AI Magazine 19(2), 79–82 (1998)Google Scholar
  16. 16.
    Wei, Y., Moreau, L., Jennings, N.: Recommender systems: A market-based design. In: Proceedings Second International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS 2003), Melbourne, Australia, July 2003, pp. 600–607 (2003)Google Scholar
  17. 17.
    Werthner, H., Ricci, F.: E-commerce and tourism. Commun. ACM 47(12), 101–105 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fabiana Lorenzi
    • 1
    • 2
  • Fabio Arreguy Camargo Correa
    • 1
  • Ana L. C. Bazzan
    • 1
  • Mara Abel
    • 1
  • Francesco Ricci
    • 3
  1. 1.Instituto de InformaticaUFRGSPorto AlegreBrazil
  2. 2.Universidade Luterana do BrasilCanoasBrazil
  3. 3.Free University of Bozen-BolzanoBolzanoItaly

Personalised recommendations