Thompson Sampling for Dynamic Pricing

In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing...

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Bibliographic Details
Published in:arXiv.org 2018-02
Main Authors: Ganti, Ravi, Matyas Sustik, Tran, Quoc, Seaman, Brian
Format: Article
Language:English
Subjects:
Active learning
Algorithms
Electronic commerce
Machine learning
Parameters
Pricing
Pricing policies
Sampling
Websites
Online Access:Get full text
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Summary:In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.
ISSN:2331-8422