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Deep Design: Product Aesthetics for Heterogeneous Markets

Authors:

Alexander Burnap,

Jeffrey Hartley,

Richard Gonzalez,

Panos Y. PapalambrosAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1961 - 1970

https://doi.org/10.1145/3097983.3098176

Published: 13 August 2017 Publication History

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.

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Cited By

Du YLiu XCai MPark K(2024)A Product’s Kansei Appearance Design Method Based on Conditional-Controlled AI Image GenerationSustainability10.3390/su1620883716:20(8837)Online publication date: 12-Oct-2024
https://doi.org/10.3390/su16208837
Zhang X(2024)Research on Product Model Generation and Visual Presentation Incorporating Computational AestheticsApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-22409:1Online publication date: 5-Aug-2024
https://doi.org/10.2478/amns-2024-2240
Diels CShu YStylids KHarrow D(2024)Designing Perceived Safety in Autonomous Vehicles: A Data-Informed-Design Approach Sizing and Shaping the Design SpaceAdjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3641308.3685017(21-26)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3641308.3685017
Show More Cited By

Index Terms

Deep Design: Product Aesthetics for Heterogeneous Markets
1. Applied computing
  1. Operations research
    1. Consumer products
  2. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 13 August 2017

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National Science Foundation

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KDD '17

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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August 3 - 7, 2025

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Cited By

Du YLiu XCai MPark K(2024)A Product’s Kansei Appearance Design Method Based on Conditional-Controlled AI Image GenerationSustainability10.3390/su1620883716:20(8837)Online publication date: 12-Oct-2024
https://doi.org/10.3390/su16208837
Zhang X(2024)Research on Product Model Generation and Visual Presentation Incorporating Computational AestheticsApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-22409:1Online publication date: 5-Aug-2024
https://doi.org/10.2478/amns-2024-2240
Diels CShu YStylids KHarrow D(2024)Designing Perceived Safety in Autonomous Vehicles: A Data-Informed-Design Approach Sizing and Shaping the Design SpaceAdjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3641308.3685017(21-26)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3641308.3685017
Shi YGao TJiao XCao N(2023)Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature ReviewProceedings of the ACM on Human-Computer Interaction10.1145/36102177:CSCW2(1-35)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610217
Tiady SMajumder AGupta DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)PRODIGY: Product Design Guidance at ScaleProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615494(4836-4842)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615494
Huang JChen BYan ZOunis IWang J(2023)GEO: A Computational Design Framework for Automotive Exterior FaceliftACM Transactions on Knowledge Discovery from Data10.1145/357852117:6(1-20)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1145/3578521
BRAUMANDL APONNRAJ ABRÜCKEL JBAUSE KALBERS A(2023)UTILISING ARTIFICIAL INTELLIGENCE TO INVESTIGATE THE RELEVANCE OF CUSTOMER BENEFITSInternational Journal of Innovation Management10.1142/S136391962340006627:05Online publication date: 31-Oct-2023
https://doi.org/10.1142/S1363919623400066
Qian YXiong YWang YJiang YLiu YChai Y(2022)Identification of Key Features for VR Applications with VREVIEW: A Topic Model Approach2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW55335.2022.00046(183-188)Online publication date: Mar-2022
https://doi.org/10.1109/VRW55335.2022.00046
Wang HChen C(2022)Using t-SNE to Evaluate the Brand Style of New Mice Design2022 IEEE International Conference on Consumer Electronics - Taiwan10.1109/ICCE-Taiwan55306.2022.9869283(451-452)Online publication date: 6-Jul-2022
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Huang JChen BLuo LYue SOunis I(2022)DVM-CAR: A Large-Scale Automotive Dataset for Visual Marketing Research and Applications2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020634(4140-4147)Online publication date: 17-Dec-2022
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