ONLINE LEARNING FOR REVENUE MANAGEMENT PROBLEMS WITH ADD-ON DISCOUNT
Abstract: In E-commerce industry, many sellers use product recommendation to enhance their profit. One of the recommendation methods is that they offer discounts on the supportive product (e.g. games) if the customer purchase the core products (e.g. game consoles). So called add-on discount. This paper mainly discusses the the revenue management problem related to the product recommendation policy with add-on discount. We formulate this problem as an optimization problem to determine the prices of different products and the selection of products with add-on discounts. We propose an upper confidence bound (UCB) learning algorithm to solve the optimization problem efficiently. In order to overcome the curse of dimensionality problem we come across when we use the learning algorithm, we propose the fully polynomial time approximation scheme (FPTAS) algorithm as a subroutine. We can show that the learning algorithm can converge to the optimal algorithm that has access to the true demand functions. And we prove that the convergence rate is tight up to a certain logarithmic term.
Advisor information: Huanan Zhang Assistant Professor of Industrial and Manufacturing Engineering
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|Work Title||ONLINE LEARNING FOR REVENUE MANAGEMENT PROBLEMS WITH ADD-ON DISCOUNT|
|License||All rights reserved|
|Work Type||Research Paper|
|Publication Date||December 4, 2020|
|Deposited||December 04, 2020|
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