IJCAI 2021poster1 citations
Jointly Learning Prices and Product Features
Ehsan Emamjomeh-Zadeh, Renato Paes Leme, Jon Schneider, Balasubramanian Sivan
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.
Machine Learning: Online LearningAgent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems
BibTeX
@inproceedings{ijcai2021p325,
title = {Jointly Learning Prices and Product Features},
author = {Emamjomeh-Zadeh, Ehsan and Paes Leme, Renato and Schneider, Jon and Sivan, Balasubramanian},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {2360--2366},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/325},
url = {https://doi.org/10.24963/ijcai.2021/325},
}