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

2 Citations (Scopus)

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)
Volume2A
ISBN (Print)9780791846315
DOIs
StatePublished - 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

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

Fingerprint

Prediction
Passenger cars
High-dimensional
Boltzmann Machine
Consumer Behaviour
Sparse Coding
Logit Model
Market Model
Consumer behavior
Machine Learning
High Accuracy
Complement
Transform
Binary
Learning systems
Model
Learning
Design
Market
Community

ASJC Scopus subject areas

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

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 (Vol. 2A). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2014-35440

Improving preference prediction accuracy with feature learning. / Burnap, Alex; Ren, Yi; Lee, Honglak; Gonzalez, Richard; Papalambros, Panos Y.

40th Design Automation Conference. Vol. 2A American Society of Mechanical Engineers (ASME), 2014.

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

Burnap, A, Ren, Y, Lee, H, Gonzalez, R & Papalambros, PY 2014, Improving preference prediction accuracy with feature learning. in 40th Design Automation Conference. vol. 2A, American Society of Mechanical Engineers (ASME), ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014, Buffalo, United States, 8/17/14. https://doi.org/10.1115/DETC2014-35440
Burnap A, Ren Y, Lee H, Gonzalez R, Papalambros PY. Improving preference prediction accuracy with feature learning. In 40th Design Automation Conference. Vol. 2A. American Society of Mechanical Engineers (ASME). 2014 https://doi.org/10.1115/DETC2014-35440
Burnap, Alex ; Ren, Yi ; Lee, Honglak ; Gonzalez, Richard ; Papalambros, Panos Y. / Improving preference prediction accuracy with feature learning. 40th Design Automation Conference. Vol. 2A American Society of Mechanical Engineers (ASME), 2014.
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