Mining Contextual Preference Rules for Building User Profiles

  • Sandra de Amo
  • Mouhamadou Saliou Diallo
  • Cheikh Talibouya Diop
  • Arnaud Giacometti
  • Haoyuan D. Li
  • Arnaud Soulet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7448)

Abstract

The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying properties of soundness and conciseness. We evaluate the efficacy of the proposed method in a series of experiments executed on a real-world database of user preferences about movies.

Keywords

Association Rule User Preference Preference Elicitation Minimal Support Threshold Preference Rule 
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.

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References

  1. 1.
    Agrawal, R., Rantzau, R., Terzi, E.: Context-sensitive ranking. In: SIGMOD Conference, pp. 383–394. ACM (2006)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)Google Scholar
  3. 3.
    Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. 21, 135–191 (2004)MathSciNetMATHGoogle Scholar
  4. 4.
    Bringmann, B., Zimmermann, A.: The chosen few: On identifying valuable patterns. In: ICDM, pp. 63–72. IEEE Computer Society (2007)Google Scholar
  5. 5.
    Burges, C.J.C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.N.: Learning to rank using gradient descent. In: ICML, vol. 119, pp. 89–96. ACM (2005)Google Scholar
  6. 6.
    Carr, R.D., Doddi, S., Konjevod, G., Marathe, M.V.: On the red-blue set cover problem. In: SODA, pp. 345–353 (2000)Google Scholar
  7. 7.
    Crammer, K., Singer, Y.: Pranking with ranking. In: NIPS, pp. 641–647. MIT Press (2001)Google Scholar
  8. 8.
    Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2003)MathSciNetGoogle Scholar
  9. 9.
    Holland, S., Ester, M., Kießling, W.: Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 204–216. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Jiang, B., Pei, J., Lin, X., Cheung, D.W., Han, J.: Mining preferences from superior and inferior examples. In: KDD, pp. 390–398. ACM (2008)Google Scholar
  11. 11.
    Joachims, T.: Optimizing search engines using clickthrough data. In: KDD, pp. 133–142. ACM (2002)Google Scholar
  12. 12.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD, pp. 80–86 (1998)Google Scholar
  13. 13.
    Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov. 1(3), 241–258 (1997)CrossRefGoogle Scholar
  14. 14.
    Peralta, V., Kostadinov, D., Bouzeghoub, M.: APMD-workbench: A benchmark for query personalization. In: Proceedings of the CIRSE Workshop (2009)Google Scholar
  15. 15.
    Song, R., Guo, Q., Zhang, R., Xin, G., Wen, J.-R., Yu, Y., Hon, H.-W.: Select-the-best-ones: A new way to judge relative relevance. Information Processing and Management 47(1), 37–52 (2011)CrossRefGoogle Scholar
  16. 16.
    Xu, J., Li, H.: AdaRank: a boosting algorithm for information retrieval. In: SIGIR, pp. 391–398. ACM (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sandra de Amo
    • 1
  • Mouhamadou Saliou Diallo
    • 2
    • 3
  • Cheikh Talibouya Diop
    • 3
  • Arnaud Giacometti
    • 2
  • Haoyuan D. Li
    • 2
  • Arnaud Soulet
    • 2
  1. 1.Universidade Federal de UberlândiaBrazil
  2. 2.Université de ToursFrance
  3. 3.Université Gaston Berger de Saint-LouisSénégal

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