---
title: "Jointly Learning Prices and Product Features"
authors:
  - "Ehsan Emamjomeh-Zadeh"
  - "Renato Paes Leme"
  - "Jon Schneider"
  - "Balasubramanian Sivan"
doi: "10.24963/ijcai.2021/325"
url: "https://ijcai.org/proceedings/2021/325"
pdf: "https://ijcai.org/proceedings/2021/0325.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "2360-2366"
track: "Main Track"
keywords:
  - "Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems"
  - "Machine Learning: Online Learning"
---

# Jointly Learning Prices and Product Features

Ehsan Emamjomeh-Zadeh, Renato Paes Leme, Jon Schneider, Balasubramanian Sivan

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 2360-2366. DOI: [10.24963/ijcai.2021/325](https://doi.org/10.24963/ijcai.2021/325)

## 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.