Yanxin Pan, Alex Burnap, J. Hartley, Rich Gonzalez, P. Papalambros
{"title":"Deep Design: Product Aesthetics for Heterogeneous Markets","authors":"Yanxin Pan, Alex Burnap, J. Hartley, Rich Gonzalez, P. Papalambros","doi":"10.1145/3097983.3098176","DOIUrl":null,"url":null,"abstract":"Aesthetic appeal is a primary driver of customer consideration for products such as automobiles. Product designers must accordingly convey design attributes (e.g., 'Sportiness'), a challenging proposition given the subjective nature of aesthetics and heterogeneous market segments with potentially different aesthetic preferences. We introduce a scalable deep learning approach that predicts how customers across different market segments perceive aesthetic designs and provides a visualization that can aid in product design. We tested this approach using a large-scale product design and crowdsourced customer data set with a Siamese neural network architecture containing a pair of conditional generative adversarial networks. The results show that the model predicts aesthetic design attributes of customers in heterogeneous market segments and provides a visualization of these aesthetic perceptions. This suggests that the proposed deep learning approach provides a scalable method for understanding customer aesthetic perceptions.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3097983.3098176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
Abstract
Aesthetic appeal is a primary driver of customer consideration for products such as automobiles. Product designers must accordingly convey design attributes (e.g., 'Sportiness'), a challenging proposition given the subjective nature of aesthetics and heterogeneous market segments with potentially different aesthetic preferences. We introduce a scalable deep learning approach that predicts how customers across different market segments perceive aesthetic designs and provides a visualization that can aid in product design. We tested this approach using a large-scale product design and crowdsourced customer data set with a Siamese neural network architecture containing a pair of conditional generative adversarial networks. The results show that the model predicts aesthetic design attributes of customers in heterogeneous market segments and provides a visualization of these aesthetic perceptions. This suggests that the proposed deep learning approach provides a scalable method for understanding customer aesthetic perceptions.