A Machine Learning Approach to Conjoint Analysis

Chapelle, O; Saul,; L.K.,; Weiss, Y.; Bottou, L.

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A Machine Learning Approach to Conjoint Analysis

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://papers.nips.cc/paper/2725-a-machine-learning-approach-to-conjoint-analysis.pdf
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Citation

Chapelle, O. (2005). A Machine Learning Approach to Conjoint Analysis. In L. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in Neural Information Processing Systems 17 (pp. 257-264). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D515-C

Abstract

Choice-based conjoint analysis builds models of consumers preferences over products with answers gathered in questionnaires. Our main goal is to bring tools from the machine learning community to solve more efficiently this problem. Thus, we propose two algorithms to estimate quickly and accurately consumer preferences.