A Knowledge Based Recommender System Based on Consistent Preference Relations

  • Luis Martínez
  • Luis G. Pérez
  • Manuel J. Barranco
  • M. Espinilla

E-commerce companies have developed many methods and tools in order to personalize their web sites and services according to users’ necessities and tastes. The most successful and widespread are the recommender systems. The aim of these systems is to lead people to interesting items through recommendations. Sometimes, these systems face situations in which there is a lack of information and this implies unsuccessful results. In this chapter we propose a knowledge based recommender system designed to overcome these situations. The proposed system is able to compute recommendations from scarce information. Our proposal will consist in gathering user’s preference information over several examples using an incomplete preference relation. The system will complete this relation and exploit it in order to obtain a user profile that will be utilized to generate good recommendations.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S. Alonso, F. Chiclana, F. Herrera, E. Herrera-Viedma, J. Alcalá-Fdez, and C. Porcel. A consistency-based procedure to estimate missing pairwise preference values. International Journal of Intelligent Systems, 23(2):155–175, 2008.MATHCrossRefGoogle Scholar
  2. 2.
    B. De Baets, B. Van de Walle, and E. Kerre. Fuzzy preference structures without incomparability. Fuzzy Sets and Systems, (76):333–348, 1995.Google Scholar
  3. 3.
    C. Basu, H. Hirsh, and W. Cohen. Recommendation as classification: Using social and contentbased information in recommendation. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 714–720, 1998.Google Scholar
  4. 4.
    R. Burke. Knowledge-based recommender systems. Encyclopedia of Library and Information Systems, 69(32), 2000.Google Scholar
  5. 5.
    R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331–370, 2002.MATHCrossRefGoogle Scholar
  6. 6.
    F. Chiclana, F. Herrera, and E. Herrera-Viedma. Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations. Fuzzy Sets and Systems, (97):33–48, 1998.Google Scholar
  7. 7.
    D. Goldberg, D. Nichols, B.M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61–70, 1992.CrossRefGoogle Scholar
  8. 8.
    H.R. Guttman. Merchant Differentiation through Integrative Negotiation in Agent-mediated Electronic Commerce. Master’s thesis, School of Architecture and Planning, Program in Media Arts and Sciences, Massachusetts Institute of Technology, 1998.Google Scholar
  9. 9.
    E. Herrera-Viedma, F. Herrera, F. Chiclana, and M. Luque. Some issues on consistency of fuzzy preference relations. European Journal of Operational Research, (154):98–109, 2004.Google Scholar
  10. 10.
    J. Kacprzyk. Group decision making with a fuzzy linguistic majority. Fuzzy Sets and Systems, 18(2):105–118, 1986.MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    J. Kolodner. Case-Based Reasoning. Morgan Kaufmann, 1993.Google Scholar
  12. 12.
    B. Krulwich. Lifestyle finder: intelligent user profiling using large-scale demographic data. AI Magazine, 18(2):37–45, 1997.Google Scholar
  13. 13.
    R.D. Luce and P. Suppes. Handbook of Mathematical Psychology, chapter Preferences, Utility and Subject Probability, pages 249–410. Wiley, New York, 1965.MATHGoogle Scholar
  14. 14.
    L. Martínez, L.G. Pérez, and M. Barranco. A multi-granular linguistic content-based recommendation model. International Journal of Intelligent Systems, 22(5):419–434, 2007.CrossRefGoogle Scholar
  15. 15.
    R.J. Mooney and L. Roy. Content-based book recommending using learning text categorization. In Proceedings of the Fifth ACM Conference on Digital Libraries, pages 195–204, 2000.Google Scholar
  16. 16.
    S.A. Orlovsky. Decision-making with a fuzzy preference relation. Fuzzy Sets Systems, 1:155–167, 1978.MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    B.M. Sarwar, G. Karypis, J.A. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In ACM Conference on Electronic Commerce, pages 158–167, 2000.Google Scholar
  18. 18.
    T. Tanino. Non-Conventional Preference Relations in Decision Making, chapter Fuzzy Preference Relations in Group Decision Making, pages 54–71. Springer-Verlag, New York, 1988.Google Scholar
  19. 19.
    Y. Wang, Y. Chuang, M. Hsu, and H. Keh. A personalized recommender system for cosmetic business. Expert Systems with Applications, (26):427–434, 2004.Google Scholar
  20. 20.
    R.R. Yager. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man and Cybernetics, 18(1):183–190, 1988.MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    R.R. Yager. Induced aggregation operators. Fuzzy Sets and Systems, 137(1): 59–69, 2003.MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Y. Yang. Expert network: Effective and efficient learning from human decisions in text categorization and retrieval. In Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 3-6 July 1994 (Special Issue of the SIGIR Forum), pages 13–22. ACM/Springer, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Luis Martínez
    • 1
  • Luis G. Pérez
    • 1
  • Manuel J. Barranco
    • 1
  • M. Espinilla
    • 1
  1. 1.Department of Computer ScienceUniversity of Jaén, JaénJaénSpain

Personalised recommendations