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dc.contributor.advisorJohn D. C. Little.en_US
dc.contributor.authorSong, Xiang, Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherSloan School of Management.en_US
dc.date.accessioned2016-06-22T17:46:50Z
dc.date.available2016-06-22T17:46:50Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/103204
dc.descriptionThesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2016.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 37-38).en_US
dc.description.abstractA 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.statementofresponsibilityby Xiang Song.en_US
dc.format.extent38 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.titleA Bayesian bandit approach to personalized online coupon recommendationsen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Management Researchen_US
dc.contributor.departmentSloan School of Management
dc.identifier.oclc951472390en_US


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