TY - GEN
T1 - Improving preference prediction accuracy with feature learning
AU - Burnap, Alex
AU - Ren, Yi
AU - Lee, Honglak
AU - Gonzalez, Richard
AU - Papalambros, Panos Y.
N1 - Publisher Copyright:
Copyright © 2014 by ASME.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84926178722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84926178722&partnerID=8YFLogxK
U2 - 10.1115/DETC2014-35440
DO - 10.1115/DETC2014-35440
M3 - Conference contribution
AN - SCOPUS:84926178722
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 40th Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014
Y2 - 17 August 2014 through 20 August 2014
ER -