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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Rantzau, R., Terzi, E.: Context-sensitive ranking. In: SIGMOD Conference, pp. 383–394. ACM (2006)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)
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)
Bringmann, B., Zimmermann, A.: The chosen few: On identifying valuable patterns. In: ICDM, pp. 63–72. IEEE Computer Society (2007)
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)
Carr, R.D., Doddi, S., Konjevod, G., Marathe, M.V.: On the red-blue set cover problem. In: SODA, pp. 345–353 (2000)
Crammer, K., Singer, Y.: Pranking with ranking. In: NIPS, pp. 641–647. MIT Press (2001)
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2003)
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)
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)
Joachims, T.: Optimizing search engines using clickthrough data. In: KDD, pp. 133–142. ACM (2002)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD, pp. 80–86 (1998)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov. 1(3), 241–258 (1997)
Peralta, V., Kostadinov, D., Bouzeghoub, M.: APMD-workbench: A benchmark for query personalization. In: Proceedings of the CIRSE Workshop (2009)
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)
Xu, J., Li, H.: AdaRank: a boosting algorithm for information retrieval. In: SIGIR, pp. 391–398. ACM (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)