Optimizing Product Assortments with Unknown Historical Transaction Data Using Nonparametric Choice Modeling and Random Forest Classification
Assortment optimization is a crucial problem for many firms who need to make decisions on which products to stock in their stores in order to maximize revenues. Optimizing assortments usually entails fitting choice models to historical data. To a large extent, this becomes a problem of understanding...
| Main Authors: | , |
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2019
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| Subjects: | |
| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261636 |