Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models

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Joachim Giesen, Klaus Mueller, Bilyana Taneva, Peter Zolliker : Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 297-315.



Conjoint analysis is a family of techniques that originated in psychology and later became popular in market research. The main objective of conjoint analysis is to measure an individual’s or a population’s preferences on a class of options that can be described by parameters and their levels. We consider preference data obtained in choice-based conjoint analysis studies, where one observes test persons’ choices on small subsets of the options. There are many ways to analyze choice-based conjoint analysis data. Here we discuss the intuition behind a classification based approach, and compare this approach to one based on statistical assumptions (discrete choice models) and to a regression approach. Our comparison on real and synthetic data indicates that the classification approach outperforms the discrete choice models.

Extended Abstract


booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
title={Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models},
url={, },
publisher={Springer Berlin Heidelberg},
author={Giesen, Joachim and Mueller, Klaus and Taneva, Bilyana and Zolliker, Peter},

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