Header logo is ei

A Machine Learning Approach to Conjoint Analysis

2005

Conference Paper

ei


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.

Author(s): Chapelle, O. and Harchaoui, Z.
Book Title: Advances in Neural Information Processing Systems 17
Journal: Advances in Neural Information Processing Systems
Pages: 257-264
Year: 2005
Month: July
Day: 0
Editors: Saul, L.K. , Y. Weiss, L. Bottou
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Eighteenth Annual Conference on Neural Information Processing Systems (NIPS 2004)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-19534-8
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{2777,
  title = {A Machine Learning Approach to Conjoint Analysis},
  author = {Chapelle, O. and Harchaoui, Z.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 17},
  pages = {257-264},
  editors = {Saul, L.K. , Y. Weiss, L. Bottou},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jul,
  year = {2005},
  month_numeric = {7}
}