Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The linearity assumption can be mitigated by combining dependent parameters into a single one, see[5] for a practical example.
References
R.A. Bradley, M.E. Terry, Rank analysis of incomplete block designs, i. the method of paired comparisons. Biometrika 39, 324–345 (1952)
T. Evgeniou, M. Pontil, T. Poggio, Regularization networks and support vector machines. Adv. Comput. Math. 13(1), 1–50 (2000)
T. Evgeniou, C. Boussios, G. Zacharia, Generalized robust conjoint estimation. Mark. Sci. 24(3), 415–429 (2005)
J. Giesen, Choice based conjoint analysis as the geometric dual of a classification problem. Manuscript, 2009
J. Giesen, K. Mueller, E. Schuberth, L. Wang, P. Zolliker, Conjoint analysis to measure the perceived quality in volume rendering. IEEE Trans. Vis. Comput. Graph. 13(6), 1664–1671 (2007)
A.Gustafsson, A.Herrmann, F.Huber, Conjoint analysis as aninstrumentofmarketresearch practice, in Conjoint Measurement. Methods and Applications, ed. by A.Gustafsson, A.Herrmann, F.Huber (Springer, Berlin, 2000), pp. 5–45
T. Kamishima, H. Kazawa, S. Akaho, A survey and empirical comparison of object ranking methods, in Preference Learning, ed. by J.Fürnkranz, E.Hüllermeier (Springer, 2010)
R.L. Keeney, H.Raiffa, Decisions with Multiple Objectives: Preferences and Value Trade-Offs (Cambridge University Press, Cambridge, 1993)
L.L. Thurstone, A law of comparative judgement. Psychol. Rev. 34, 273–286 (1927)
K.Train, Discrete Choice Methods with Simulation (Cambridge University Press, 2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Giesen, J., Mueller, K., Taneva, B., Zolliker, P. (2010). Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models. In: Fürnkranz, J., Hüllermeier, E. (eds) Preference Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14125-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-14125-6_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14124-9
Online ISBN: 978-3-642-14125-6
eBook Packages: Computer ScienceComputer Science (R0)