Skip to main content

Mining Contextual Preference Rules for Building User Profiles

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Rantzau, R., Terzi, E.: Context-sensitive ranking. In: SIGMOD Conference, pp. 383–394. ACM (2006)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)

    Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  4. Bringmann, B., Zimmermann, A.: The chosen few: On identifying valuable patterns. In: ICDM, pp. 63–72. IEEE Computer Society (2007)

    Google Scholar 

  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. 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. Crammer, K., Singer, Y.: Pranking with ranking. In: NIPS, pp. 641–647. MIT Press (2001)

    Google Scholar 

  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)

    MathSciNet  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Joachims, T.: Optimizing search engines using clickthrough data. In: KDD, pp. 133–142. ACM (2002)

    Google Scholar 

  12. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD, pp. 80–86 (1998)

    Google Scholar 

  13. Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov. 1(3), 241–258 (1997)

    Article  Google Scholar 

  14. Peralta, V., Kostadinov, D., Bouzeghoub, M.: APMD-workbench: A benchmark for query personalization. In: Proceedings of the CIRSE Workshop (2009)

    Google Scholar 

  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)

    Article  Google Scholar 

  16. Xu, J., Li, H.: AdaRank: a boosting algorithm for information retrieval. In: SIGIR, pp. 391–398. ACM (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

de Amo, S., Diallo, M.S., Diop, C.T., Giacometti, A., Li, H.D., Soulet, A. (2012). Mining Contextual Preference Rules for Building User Profiles. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32584-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32584-7_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32583-0

  • Online ISBN: 978-3-642-32584-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics