Pairwise Preference Learning and Ranking

  • Johannes Fürnkranz
  • Eyke Hüllermeier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)


We consider supervised learning of a ranking function, which is a mapping from instances to total orders over a set of labels (options). The training information consists of examples with partial (and possibly inconsistent) information about their associated rankings. From these, we induce a ranking function by reducing the original problem to a number of binary classification problems, one for each pair of labels. The main objective of this work is to investigate the trade-off between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example. To this end, we present theoretical results on the complexity of pairwise preference learning, and experimentally investigate the predictive performance of our method for different types of preference information, such as top-ranked labels and complete rankings. The domain of this study is the prediction of a rational agent’s ranking of actions in an uncertain environment.


Ranking Function Total Order Preference Information Round Robin Expected Utility Theory 
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.


  1. 1.
    Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141 (2000)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Cooper, G.F., Moral, S. (eds.) Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998), Madison, WI, pp. 43–52. Morgan Kaufmann, San Francisco (1998)Google Scholar
  3. 3.
    Fürnkranz, J.: Round robin classification. Journal of Machine Learning Research 2, 721–747 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    Fürnkranz, J.: Round robin ensembles. Intelligent Data Analysis 7(5) (2003) (to appear) Google Scholar
  5. 5.
    Fürnkranz, J., Hüllermeier, E.: Pairwise preference learning and ranking. Technical Report OEFAI-TR-2003-14, Austrian Research Institute for Artificial Intelligence, Wien, Austria (2003)Google Scholar
  6. 6.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave and information tapestry. Communications of the ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  7. 7.
    Har-Peled, S., Roth, D., Zimak, D.: Constraint classification: A new approach to multiclass classification. In: Cesa-Bianchi, N., Numao, M., Reischuk, R. (eds.) ALT 2002. LNCS (LNAI), vol. 2533, pp. 365–379. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: Jordan, M., Kearns, M., Solla, S. (eds.) Advances in Neural Information Processing Systems 10 (NIPS 1997), pp. 507–513. MIT Press, Cambridge (1998)Google Scholar
  9. 9.
    Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: Solla, S.A., Leen, T.K., Müller, K.-R. (eds.) Advances in Neural Information Processing Systems 12 (NIPS 1999), pp. 547–553. MIT Press, Cambridge (2000)Google Scholar
  10. 10.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, SanMateo (1993)Google Scholar
  11. 11.
    Resnick, P., Varian, H.R.: Special issue on recommender systems. Communications of the ACM 40(3) (1997)Google Scholar
  12. 12.
    Savicky, P., Fürnkranz, J.: Combining pairwise classifiers with stacking. In: R. Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 219–229. Springer, Heidelberg (2003) (to appear)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Johannes Fürnkranz
    • 1
  • Eyke Hüllermeier
    • 2
  1. 1.Austrian Research Institute for Artificial IntelligenceWienAustria
  2. 2.Informatics InstituteMarburg UniversityMarburgGermany

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