Jointly Learning Prices and Product Features
Abstract
Product Design is an important problem in marketing research where a firm tries to learn what features of a product are more valuable to consumers. We study this problem from the viewpoint of online learning: a firm repeatedly interacts with a buyer by choosing a product configuration as well as a price and observing the buyer’s purchasing decision. The goal of the firm is to maximize revenue throughout the course of T rounds by learning the buyer’s preferences. We study both the case of a set of discrete products and the case of a continuous set of allowable product features. In both cases we provide nearly tight upper and lower regret bounds.
Cite
Text
Emamjomeh-Zadeh et al. "Jointly Learning Prices and Product Features." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/325Markdown
[Emamjomeh-Zadeh et al. "Jointly Learning Prices and Product Features." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/emamjomehzadeh2021ijcai-jointly/) doi:10.24963/IJCAI.2021/325BibTeX
@inproceedings{emamjomehzadeh2021ijcai-jointly,
title = {{Jointly Learning Prices and Product Features}},
author = {Emamjomeh-Zadeh, Ehsan and Leme, Renato Paes and Schneider, Jon and Sivan, Balasubramanian},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {2360-2366},
doi = {10.24963/IJCAI.2021/325},
url = {https://mlanthology.org/ijcai/2021/emamjomehzadeh2021ijcai-jointly/}
}