Improving preference prediction accuracy with feature learning

Alex Burnap, Yi Ren, Honglak Lee, Richard Gonzalez, Panos Y. Papalambros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Motivated by continued interest within the design community to model design preferences, this paper investigates the question of predicting preferences with particular application to consumer purchase behavior: How can we obtain high prediction accuracy in a consumer preference model using market purchase data? To this end, we employ sparse coding and sparse restricted Boltzmann machines, recent methods from machine learning, to transform the original market data into a sparse and high-dimensional representation. We show that these 'feature learning' techniques, which are independent from the preference model itself (e.g., logit model), can complement existing efforts towards high-accuracy preference prediction. Using actual passenger car market data, we achieve significant improvement in prediction accuracy on a binary preference task by properly transforming the original consumer variables and passenger car variables to a sparse and high-dimensional representation.

Original languageEnglish (US)
Title of host publication40th Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791846315
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
EventASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014 - Buffalo, United States
Duration: Aug 17 2014Aug 20 2014

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2A

Other

OtherASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014
CountryUnited States
CityBuffalo
Period8/17/148/20/14

ASJC Scopus subject areas

  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Burnap, A., Ren, Y., Lee, H., Gonzalez, R., & Papalambros, P. Y. (2014). Improving preference prediction accuracy with feature learning. In 40th Design Automation Conference (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2A). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2014-35440