Preference Learning

Tutorial at ECAI-12, Montpellier


The primary goal of this tutorial is to survey the field of preference learning in its current stage of development. The presentation will focus on a systematic overview of different types of preference learning problems, methods and algorithms to tackle these problems, and metrics for evaluating the performance of preference models induced from data.


We will cover the following topics

  1. Preference Learning Tasks
    • Introduction to Preference Learning
    • Object Ranking
    • Instance Ranking
    • Label Ranking
  2. Performance Assessment and Loss Functions
    • ranking errors (Spearman, Kendall's tau, ...)
    • multipartite ranking measures (AUC, C-index, ...)
    • information retrieval measures (precision@k, NCDG, ...)
  3. Preference Learning Techniques
    • learning utility functions
    • learning preference relations
    • model-based preference learning
    • local aggregation of preferences
  4. Complexity of Preference Learning
    • training complexity
    • prediction complexity
  5. Conclusions


    Slides of the tutorials will be made available at this site.

    The above outline essentially follows the introductory chapter of the following book on preference learning.

  • J. Fürnkranz and E. Hüllermeier, Preference Learning: An Introduction. In Fürnkranz & Hüllermeier (eds.) Preference Learning, Springer-Verlag, 2010.


Related Events

There will be several events related to preferences and preferences learning at ECAI-12: