A Bayesian bandit approach to personalized online coupon recommendations

Author(s)
Song, Xiang, Ph. D. Massachusetts Institute of Technology
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Other Contributors
Sloan School of Management.
Advisor
John D. C. Little.
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Abstract
A digital coupon distributing firm selects coupons from its coupon pool and posts them online for its customers to activate them. Its objective is to maximize the total number of clicks that activate the coupons by sequential arriving customers. This paper resolves this problem by using a multi-armed bandit approach to balance the exploration (learning customers' preference for coupons) with exploitation (maximizing short term activation clicks). The proposed approach is evaluated with synthetic data. Results showed a 60% click lift compared to the benchmark approach.
Description
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2016.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 37-38).
 
Date issued
2016
URI
http://hdl.handle.net/1721.1/103204
Department
Sloan School of Management
Publisher
Massachusetts Institute of Technology
Keywords
Sloan School of Management.
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