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  A Machine Learning Approach to Conjoint Analysis

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D515-C Version Permalink: https://hdl.handle.net/21.11116/0000-0005-214B-2
Genre: Conference Paper

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http://papers.nips.cc/paper/2725-a-machine-learning-approach-to-conjoint-analysis.pdf (Publisher version)
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 Creators:
Chapelle, O1, Author           
Saul, Editor
L.K., Editor
Weiss, Y., Editor
Bottou, L., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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.

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 Dates: 2005-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2777
 Degree: -

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Title: Eighteenth Annual Conference on Neural Information Processing Systems (NIPS 2004)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2004-12-13 - 2004-12-16

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Title: Advances in Neural Information Processing Systems 17
Source Genre: Proceedings
 Creator(s):
Saul, LK, Editor
Weiss, Y, Editor
Bottou, L, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 257 - 264 Identifier: ISBN: 0-262-19534-8