Learning attitudinal decision model through pair-wise preferences

Abstract

This paper aims to learn a decision-maker’s (DM’s) behavioral process that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the econometric choice models and machine learning. The study is validated on a set of 12 benchmark data sets.

Design/methodology/approach The study includes emerging preference learning algorithms.

Findings Learning of a DM’s attitudinal choice model.

Originality/value Preferences-based learning of a DM’s attitudinal decision model. Empirical validation through 12 real data sets. Comparison with baseline methods.