dc.contributor.advisor | John D. C. Little. | en_US |
dc.contributor.author | Song, Xiang, Ph. D. Massachusetts Institute of Technology | en_US |
dc.contributor.other | Sloan School of Management. | en_US |
dc.date.accessioned | 2016-06-22T17:46:50Z | |
dc.date.available | 2016-06-22T17:46:50Z | |
dc.date.copyright | 2016 | en_US |
dc.date.issued | 2016 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/103204 | |
dc.description | Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2016. | en_US |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (pages 37-38). | en_US |
dc.description.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. | en_US |
dc.description.statementofresponsibility | by Xiang Song. | en_US |
dc.format.extent | 38 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | 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. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Sloan School of Management. | en_US |
dc.title | A Bayesian bandit approach to personalized online coupon recommendations | en_US |
dc.type | Thesis | en_US |
dc.description.degree | S.M. in Management Research | en_US |
dc.contributor.department | Sloan School of Management | |
dc.identifier.oclc | 951472390 | en_US |