Product Design Using a Generative Adversarial Network: Incorporating Consumer Preferences and External Data

46 Pages Posted: 29 May 2024

See all articles by Hui Li

Hui Li

The University of Hong Kong

Jian Ni

Virginia Tech - Pamplin College of Business

Fangzhu Yang

Johns Hopkins University

Date Written: May 24, 2024

Abstract

The development of generative artificial intelligence (AI) enables large-scale product design automation. However, this automated process usually does not incorporate consumer preference information from the internal dataset of a company. Furthermore, external sources such as social media and user-generated content (UGC) websites often contain rich product design and consumer preference information, but such information is not utilized by companies when generating designs. We propose a semi-supervised deep generative framework that integrates consumer preferences and external data into the product design process, allowing companies to generate consumer-preferred designs in a cost-effective and scalable way. We train a predictor model to learn consumer preferences and use predicted popularity levels as additional input labels to guide the training procedure of a continuous conditional generative adversarial network (Cc-GAN). The CcGAN can be instructed to generate new designs with a certain popularity level, enabling companies to efficiently create consumer-preferred designs and save resources by avoiding the development and testing of unpopular designs. The framework also incorporates existing product designs and consumer preference information from external sources, which is particularly helpful for small or start-up companies that have limited internal data and face the "cold-start" problem. We apply the proposed framework to a real business setting by helping a large self-aided photography chain in China design new photo templates. We show that our proposed model performs well in terms of generating appealing template designs for the company.

Keywords: Product design, generative models, deep learning, user-generated content (UGC)

JEL Classification: D12, L86, M31

Suggested Citation

Li, Hui and Ni, Jian and Yang, Fangzhu, Product Design Using a Generative Adversarial Network: Incorporating Consumer Preferences and External Data (May 24, 2024). Available at SSRN: https://ssrn.com/abstract=4840098 or http://dx.doi.org/10.2139/ssrn.4840098

Hui Li

The University of Hong Kong ( email )

4/F., K.K. Leung Building
The University of Hong Kong
Hong Kong

Jian Ni (Contact Author)

Virginia Tech - Pamplin College of Business ( email )

1016 Pamplin Hall
Blacksburg, VA 24061
United States

HOME PAGE: http://https://sites.google.com/site/jiannicmu/

Fangzhu Yang

Johns Hopkins University ( email )

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