Advertisement

Probabilistic User Modeling in the Presence of Drifting Concepts

  • Vikas Bhardwaj
  • Ramaswamy Devarajan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6118)

Abstract

We investigate supervised prediction tasks which involve multiple agents over time, in the presence of drifting concepts. The motivation behind choosing the topic is that such tasks arise in many domains which require predicting human actions. An example of such a task is recommender systems, where it is required to predict the future ratings, given features describing items and context along with the previous ratings assigned by the users. In such a system, the relationships among the features and the class values can vary over time. A common challenge to learners in such a setting is that this variation can occur both across time for a given agent, and also across different agents, (i.e. each agent behaves differently). Furthermore, the factors causing this variation are often hidden. We explore probabilistic models suitable for this setting, along with efficient algorithms to learn the model structure. Our experiments use the Netflix Prize dataset, a real world dataset which shows the presence of time variant concepts. The results show that the approaches we describe are more accurate than alternative approaches, especially when there is a large variation among agents. All the data and source code would be made open-source under the GNU GPL.

Keywords

Hide Markov Model Recommender System Homogeneous Model Collaborative Filter Concept Drift 
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.
    Case, J., Jain, S., Kaufmann, S., Sharma, A., Stephan, F.: Predictive learning models for concept drift. In: Richter, M.M., Smith, C.H., Wiehagen, R., Zeugmann, T. (eds.) ALT 1998. LNCS (LNAI), vol. 1501, pp. 276–290. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  2. 2.
    Coulondre, S., Simonin, O., Ferber, J.: Dynamo: a behavioural analysis model for multi-agent systems. In: Proceedings 1999 International Conference on Information Intelligence and Systems, pp. 614–621 (1999)Google Scholar
  3. 3.
    Delcher, A., Kasif, S., Fleischmann, R., Peterson, J., White, O., Salzberg, S.: Alignment of whole genomes. Nucleic Acids Research 27(11), 2369–2376 (1999)CrossRefGoogle Scholar
  4. 4.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39, 1–38 (1977)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Helmbold, D.P., Long, P.M.: Tracking drifting concepts by minimizing disagreements. Machine Learning, 27–45 (1994)Google Scholar
  6. 6.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 230–237. ACM, New York (1999)CrossRefGoogle Scholar
  7. 7.
    Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 97–106. ACM, New York (2001)CrossRefGoogle Scholar
  8. 8.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426–434. ACM, New York (2008)CrossRefGoogle Scholar
  9. 9.
    Lane, T., Brodley, C.E.: Approaches to online learning and concept drift for user identification in computer security. In: KDD, pp. 259–263. AAAI Press, Menlo ParkGoogle Scholar
  10. 10.
    Leung, C.W.-k., Chan, S.C.-f., Chung, F.-l.: A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl. Inf. Syst. 10(3), 357–381 (2006)CrossRefGoogle Scholar
  11. 11.
    Núńez, M., Fidalgo, R., Morales, R.: Learning in environments with unknown dynamics: Towards more robust concept learners. J. Mach. Learn. Res. 8, 2595–2628 (2007)MathSciNetGoogle Scholar
  12. 12.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  13. 13.
    Virtanen, T., Heittola, T.: Interpolating hidden markov model and its application to automatic instrument recognition. In: ICASSP 2009: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Washington, DC, USA, pp. 49–52. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  14. 14.
    Wang, Y., Zhou, L., Feng, J., Wang, J., Liu, Z.-Q.: Mining complex time-series data by learning markovian models. In: ICDM 2006: Proceedings of the Sixth International Conference on Data Mining, Washington, DC, USA, pp. 1136–1140. IEEE Computer Society, Los Alamitos (2006)CrossRefGoogle Scholar
  15. 15.
    Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)Google Scholar
  16. 16.
    Zhang, P., Zhu, X., Shi, Y.: Categorizing and mining concept drifting data streams. In: KDD 2008: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 812–820. ACM, New York (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Vikas Bhardwaj
    • 1
  • Ramaswamy Devarajan
    • 1
  1. 1.Department of Computer ScienceColumbia UniversityNew YorkUSA

Personalised recommendations