Preference Learning

@inproceedings{Frnkranz2005PreferenceL,
  title={Preference Learning},
  author={Johannes F{\"u}rnkranz and Eyke H{\"u}llermeier},
  booktitle={K{\"u}nstliche Intell.},
  year={2005}
}
By combining practical relevance with novel types of prediction problems, the learning from/of preferences has recently received a lot of attention in the machine learning literature. Just as other types of complex learning tasks, preference learning deviates strongly from the standard problems of classification and regression. It is particularly challenging because it involves the prediction of complex structures, such as weak or partial order relations, rather than single values. This article… 
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References

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TLDR
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.
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TLDR
An approach is presented that performs a linear mapping from objects to utility values and thus guarantees transitivity and antisymmetry and is extended to nonlinear utility functions by using the potential function method, which allows to incorporate higher order correlations of features into the utility func tion at minimal computational costs.
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A new training paradigm, called the "comparison paradigm," is introduced for tasks in which a network must learn to choose a preferred pattern from a set of n alternatives, based on examples of human
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TLDR
An on-line algorithm for learning preference functions that is based on Freund and Schapire's "Hedge" algorithm is considered, and it is shown that the problem of finding the ordering that agrees best with a learned preference function is NP-complete.
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The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking.
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Prospects for Preferences
  • J. Doyle
  • Economics, Psychology
    Comput. Intell.
  • 2004
This article examines prospects for theories and methods of preferences, both in the specific sense of the preferences of the ideal rational agents considered in economics and decision theory and in
Learning to order things Prospects for Preferences Pairwise preference learning and ranking
    FG Knowledge Engineering Hochschulstr. 10, 64289 Darmstadt Email: fuernkranz@informatik.tu-darmstadt.de http://www.ke.informatik.tu-darmstadt
    • FG Knowledge Engineering Hochschulstr. 10, 64289 Darmstadt Email: fuernkranz@informatik.tu-darmstadt.de http://www.ke.informatik.tu-darmstadt