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: |
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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}, doi = {}, month_numeric = {7} } |