A Machine Learning Approach to Conjoint Analysis
Abstract
Choice-based conjoint analysis builds models of consumer preferences over products with answers gathered in questionnaires. Our main goal is to bring tools from the machine learning community to solve this prob- lem more efficiently. Thus, we propose two algorithms to quickly and accurately estimate consumer preferences.
Cite
Text
Chapelle and Harchaoui. "A Machine Learning Approach to Conjoint Analysis." Neural Information Processing Systems, 2004.Markdown
[Chapelle and Harchaoui. "A Machine Learning Approach to Conjoint Analysis." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/chapelle2004neurips-machine/)BibTeX
@inproceedings{chapelle2004neurips-machine,
title = {{A Machine Learning Approach to Conjoint Analysis}},
author = {Chapelle, Olivier and Harchaoui, Zaïd},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {257-264},
url = {https://mlanthology.org/neurips/2004/chapelle2004neurips-machine/}
}