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Preference Learning and Ranking by Pairwise Comparison

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Preference Learning

Abstract

This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.

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Acknowledgements

This research has been supported by the German Science Foundation (DFG). We would like to thank our collaborators Klaus Brinker, Weiwei Cheng, Jens Hühn, Eneldo Loza Mencía, Sang-Hyeun Park, and Stijn Vanderlooy.

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Authors and Affiliations

  1. Technische Universität Darmstadt, Darmstadt, Germany

    Johannes Fürnkranz

  2. Philipps-Universität Marburg, Marburg, Germany

    Eyke Hüllermeier

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  1. Johannes Fürnkranz
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  2. Eyke Hüllermeier
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Correspondence to Johannes Fürnkranz .

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Editors and Affiliations

  1. FB Informatik, TU Darmstadt, Hochschulstr. 10, Darmstadt, 64289, Germany

    Johannes Fürnkranz

  2. FB Mathematik und Informatik, Philipps-Universität Marburg, Hans-Meerwein-Str., Marburg, 35032, Germany

    Eyke Hüllermeier

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Fürnkranz, J., Hüllermeier, E. (2010). Preference Learning and Ranking by Pairwise Comparison. In: Fürnkranz, J., Hüllermeier, E. (eds) Preference Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14125-6_4

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  • DOI: https://doi.org/10.1007/978-3-642-14125-6_4

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  • Print ISBN: 978-3-642-14124-9

  • Online ISBN: 978-3-642-14125-6

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