Advertisement

TRES: A Decentralized Agent-Based Recommender System to Support B2C Activities

  • Domenico Rosaci
  • Giuseppe M. L. Sarné
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5559)

Abstract

The increasing relevance assumed by the E-Commerce in the Web community is attested by the great number of powerful and sophisticated tools developed in the last years to support traders in their commercial activities. In this scenario, recommender systems appear doubtless as a promising solution for supporting both customers’ and merchants’ activities. In this paper, we propose an agent-based recommender system, called TRES, able to help traders in Business-to-Consumer activities with useful and personalized suggestions based on interests and preferences stored in customers’ profiles, adopting a fully decentralized architecture that suitably introduces in the system both scalability and privacy protection.

Keywords

Interest Rate Recommender System Product Category Recommendation Algorithm Product Instance 
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.
    Engel, J.F., Blackwell, R.D., Miniard, P.W.: Consumer Behaviour. The Dryden Press, London (1995)Google Scholar
  2. 2.
    Extensible Markup Language (XML) Schema (2008), http://www.w3.org/XML/Schema
  3. 3.
    Feldman, S.: The Objects of the E-Commerce, Keynote Speech at ACM 1999 Conf. on OOPLSA, Denver (1999), http://www.ibm.com/iac/oopsla99-sifkeynote.pdf
  4. 4.
    Guttman, R.H., Moukas, A., Maes, P.: Agents as Mediators in Electronic Commerce. Electronic Markets 8(1) (1998)Google Scholar
  5. 5.
    Java Agent DEvelop. framew (JADE) (2008), http://jade.tilab.com/
  6. 6.
    Jung, J.J.: Ontological Framework Based on Contextual Mediation for Collaborative Information Retrieval. Information Retrieval 10(1), 85–109 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kauffman, R.J., Walden, E.A.: Economics and Electronic Commerce: Survey and Directions for Research. Int. J. Electr. Com. 5(4), 5–116 (2001)Google Scholar
  8. 8.
    Maes, P.: Agents that Reduce Work and Information Overload. Commun. ACM 37(7), 30–40 (1994)CrossRefGoogle Scholar
  9. 9.
    De Meo, P., Rosaci, D., Sarnè, G.M.L., Ursino, D., Terracina, G.: EC-XAMAS: Supporting E-Commerce Activities by an XML-based Adaptive Multi-Agent System. Appl. Artif. Intell. 21(6), 529–562 (2007)CrossRefGoogle Scholar
  10. 10.
    Montaner, M., Lopez, B., de la Rosa, J.L.: A Taxonomy of Recommender Agents on the Internet. J. on Web Semantics (JWS) 19(4), 285–330 (2004)Google Scholar
  11. 11.
    North America Industry Classifications (NAICS) (2008), http://www.census.gov/naics/2007/index.html
  12. 12.
    Palopoli, L., Rosaci, D., Ursino, D.: Agents’ Roles in B2C E-Commerce. AI Commun. 19(2), 95–126 (2006)MathSciNetGoogle Scholar
  13. 13.
    van Rijsbergen, C.J.: Information Retrieval. Butterworth (1979)Google Scholar
  14. 14.
    Wei, K., Huang, J., Fu, S.: A Survey of E-Commerce Recommender Systems. In: Proc. of the 13th Int. Conf. on Service Systems and Service Management, Washington, DC, USA, pp. 1–5. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  15. 15.
    Zwass, V.: Electronic Commerce and Organizational Innovation: Aspects and Opportunities. Int. J. Electron. Com. 7(3), 7–37 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Domenico Rosaci
    • 1
  • Giuseppe M. L. Sarné
    • 1
  1. 1.DIMETUniversità Mediterranea di Reggio CalabriaReggio CalabriaItaly

Personalised recommendations