Journal
JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS
Volume 22, Issue 3-4, Pages 175-183Publisher
WILEY
DOI: 10.1002/mcda.1520
Keywords
preference learning; learning to rank; decision making; TOPSIS
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Funding
- German Research Foundation (DFG)
- German Academic Exchange Service (DAAD)
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Combining established modelling techniques from multiple-criteria decision aiding with recent algorithmic advances in the emerging field of preference learning, we propose a new method that can be seen as an adaptive version of TOPSIS, the technique for order preference by similarity to ideal solution decision model (or at least a simplified variant of this model). On the basis of exemplary preference information in the form of pairwise comparisons between alternatives, our method seeks to induce an 'ideal solution' that, in conjunction with a weight factor for each criterion, represents the preferences of the decision maker. To this end, we resort to probabilistic models of discrete choice and make use of maximum likelihood inference. First experimental results on suitable preference data suggest that our approach is not only intuitively appealing and interesting from an interpretation point of view but also competitive to state-of-the-art preference learning methods in terms of prediction accuracy. Copyright (C) 2014 John Wiley & Sons, Ltd.
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