2026-09-20T02:16:08Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1131762026-06-06T00:48:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Glen Urban.
Perez, Santiago(Perez Lastra)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-01-12T21:15:41Z
2018-01-12T21:15:41Z
2017
2017
http://hdl.handle.net/1721.1/113176
1017490273
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 65-66).
Modeling customer preferences over multivariate attributes has long been an endeavor of marketing research. We provide an updated approach which can learn customer preferences for complex products with multiple multivariate attributes using modern Deep Neural Networks. In turn we outline approaches for framing managerial questions in the form of inference problems. With our empirical application to product identification in credit cards, we conclude Deep Learning results in significantly better performance than the state of the art. Our approach is scalable to Big Data and can derive superior predictive power from the inexpensive unstructured data exhaust of internet commerce.
by Santiago Perez.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
66 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.
Deep learning for discovering new product opportunities
Thesis
application/pdf
2019-06-17T20:36:28Z
Publication
unknown
unknown
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