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Preferences-based learning of multinomial logit model

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Abstract

We learn the parameters of the popular multinomial logit model to gain insights about a DM’s decision process. We accomplish this objective through the recent algorithmic advances in the emerging field of preference learning. The empirical evaluation of the proposed approach is performed on a set of 12 publicly available benchmark datasets. First experimental results suggest that our approach is not only intuitively appealing, but also competitive to state-of-the-art preference learning methods in terms of the prediction accuracy.

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Notes

  1. The estimated PIS and NIS are different in each iteration depending upon the random selection of alternatives in \(\mathcal {A}_{train}\) and \(\mathcal {A}_{test}\).

  2. http://people.kyb.tuebingen.mpg.de/spider/.

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  1. Indian Institute of Management Ahmedabad, Ahmedabad, India

    Manish Aggarwal

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  1. Manish Aggarwal
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Correspondence to Manish Aggarwal.

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Aggarwal, M. Preferences-based learning of multinomial logit model. Knowl Inf Syst 59, 523–538 (2019). https://doi.org/10.1007/s10115-018-1215-9

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  • DOI: https://doi.org/10.1007/s10115-018-1215-9

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