A Bayesian bandit approach to personalized online coupon recommendations
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2016.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2016
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/103204 |
| _version_ | 1811080654141521920 |
|---|---|
| author | Song, Xiang, Ph. D. Massachusetts Institute of Technology |
| author2 | John D. C. Little. |
| author_facet | John D. C. Little. Song, Xiang, Ph. D. Massachusetts Institute of Technology |
| author_sort | Song, Xiang, Ph. D. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2016. |
| first_indexed | 2024-09-23T11:34:42Z |
| format | Thesis |
| id | mit-1721.1/103204 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:34:42Z |
| publishDate | 2016 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1032042019-04-12T15:03:56Z A Bayesian bandit approach to personalized online coupon recommendations Song, Xiang, Ph. D. Massachusetts Institute of Technology John D. C. Little. Sloan School of Management. Sloan School of Management. Sloan School of Management. 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 2016-06-22T17:46:50Z 2016-06-22T17:46:50Z 2016 2016 Thesis http://hdl.handle.net/1721.1/103204 951472390 eng 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 38 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Sloan School of Management. Song, Xiang, Ph. D. Massachusetts Institute of Technology A Bayesian bandit approach to personalized online coupon recommendations |
| title | A Bayesian bandit approach to personalized online coupon recommendations |
| title_full | A Bayesian bandit approach to personalized online coupon recommendations |
| title_fullStr | A Bayesian bandit approach to personalized online coupon recommendations |
| title_full_unstemmed | A Bayesian bandit approach to personalized online coupon recommendations |
| title_short | A Bayesian bandit approach to personalized online coupon recommendations |
| title_sort | bayesian bandit approach to personalized online coupon recommendations |
| topic | Sloan School of Management. |
| url | http://hdl.handle.net/1721.1/103204 |
| work_keys_str_mv | AT songxiangphdmassachusettsinstituteoftechnology abayesianbanditapproachtopersonalizedonlinecouponrecommendations AT songxiangphdmassachusettsinstituteoftechnology bayesianbanditapproachtopersonalizedonlinecouponrecommendations |