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Ellipsoids for contextual dynamic pricing

Ellipsoids for contextual dynamic pricing We study the dynamic pricing problem faced by a firm selling differentiated products. At each period, the firm receives a new product, which is described by a vector of features. The firm needs to choose prices, but it does not know a priori the market value of the different features. We first consider an algorithm that we call PolytopePricing, but prove that it incurs worst-case regret that scales exponentially in the dimensionality of the feature space. We then consider a closely related algorithm, EllipsoidPricing, and show it incurs low regret with regards to both the time horizon and the dimensionality of the feature space. For more details, we refer the reader to our full paper. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Ellipsoids for contextual dynamic pricing

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References (15)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 Authors
ISSN
1551-9031
eISSN
1551-9031
DOI
10.1145/3055589.3055595
Publisher site
See Article on Publisher Site

Abstract

We study the dynamic pricing problem faced by a firm selling differentiated products. At each period, the firm receives a new product, which is described by a vector of features. The firm needs to choose prices, but it does not know a priori the market value of the different features. We first consider an algorithm that we call PolytopePricing, but prove that it incurs worst-case regret that scales exponentially in the dimensionality of the feature space. We then consider a closely related algorithm, EllipsoidPricing, and show it incurs low regret with regards to both the time horizon and the dimensionality of the feature space. For more details, we refer the reader to our full paper.

Journal

ACM SIGecom ExchangesAssociation for Computing Machinery

Published: Feb 24, 2017

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