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DTSTART:20221030T030000
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UID:calendar.25656.field_data.0@www.open.diag.uniroma1.it
DTSTAMP:20260404T174504Z
CREATED:20230315T131026Z
DESCRIPTION:In this talk\, we will present new models for consumer preferen
 ce learning which is a critical task in numerous marketing and operational
  decisions. A commonly used data-driven approach for consumer preference l
 earning is based on eliciting consumer choice preferences and fitting a fu
 nction that ultimately provides a utility score for each consumer choice. 
 A major issue in this approach is the noise in choice data that is due to 
 the natural “irrationality” of consumers in their decision-making which le
 ads to inaccuracies in the learned models. We build upon ideas from machin
 e learning and mathematical programming\, and propose a robust preference 
 elicitation model that guarantees robustness against feature noise (i.e.\,
  perturbations caused by consumer misconceptions) and label noise (i.e.\, 
 response errors). For that\, we present new optimization models that captu
 re the specificity of consumer preference learning and discuss the results
  that show higher model accuracy as well as more detailed segmentation of 
 consumers.zoom link: https://uniroma1.zoom.us/j/3378957140
DTSTART;TZID=Europe/Paris:20230322T150000
DTEND;TZID=Europe/Paris:20230322T150000
LAST-MODIFIED:20230321T100631Z
LOCATION:Aula A6
SUMMARY:Optimization Models for Learning Consumer Preferences - Prof. Joe N
 aoum-Sawaya
URL;TYPE=URI:http://www.open.diag.uniroma1.it/node/25656
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