19/08/2021

Jointly Learning Prices and Product Features

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

Keywords: Machine Learning, Online Learning, Economic Paradigms, Auctions and Market-Based Systems

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.

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