2026-09-18T19:36:18Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1157292026-06-05T20:26:31Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Devavrat Shah.
Atwi, Aliaa
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-05-23T16:32:12Z
2018-05-23T16:32:12Z
2018
2018
http://hdl.handle.net/1721.1/115729
1036986657
Thesis: Elec. E. in Computer Science, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 51-52).
Personalized offers aim to maximize profit by taking into account customer preferences inferred from past purchase behavior. For large retailers with extensive product offerings, learning customer preferences can be challenging due to relatively short purchase histories of most customers. To alleviate the dearth of data, we propose exploiting similarities among products and among customers to reduce problem dimensions. We also propose that retailers use personalized offers not only to maximize expected profit, but to actively learn their customers' preferences. An offer that does not maximize expected profit given current information may still provide valuable insights about customer preferences. This information enables more profitable coupon allocation and higher profits in the long run. In this thesis we 1) derive approximate inference algorithms to learn customer preferences from purchase data in real time, 2) formulate the retailers' offer allocation problem as a multi armed bandit and explore solution strategies.
by Aliaa Atwi.
Elec.E. in Computer Science
52 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Exploration vs. exploitation in coupon personalization
Exploration versus exploitation in coupon personalization
Thesis
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<Abstract>Personalized offers aim to maximize profit by taking into account customer preferences inferred from past purchase behavior. For large retailers with extensive product offerings, learning customer preferences can be challenging due to relatively short purchase histories of most customers. To alleviate the dearth of data, we propose exploiting similarities among products and among customers to reduce problem dimensions. We also propose that retailers use personalized offers not only to maximize expected profit, but to actively learn their customers' preferences. An offer that does not maximize expected profit given current information may still provide valuable insights about customer preferences. This information enables more profitable coupon allocation and higher profits in the long run. In this thesis we 1) derive approximate inference algorithms to learn customer preferences from purchase data in real time, 2) formulate the retailers' offer allocation problem as a multi armed bandit and explore solution strategies.</Abstract>
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