2026-09-20T14:10:54Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1032042022-01-13T07:54:52Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
John D. C. Little.
Song, Xiang, Ph. D. Massachusetts Institute of Technology
Sloan School of Management.
Sloan School of Management
2016-06-22T17:46:50Z
2016-06-22T17:46:50Z
2016
2016
http://hdl.handle.net/1721.1/103204
951472390
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).
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.
by Xiang Song.
S.M. in Management Research
38 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
A Bayesian bandit approach to personalized online coupon recommendations
Thesis
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