  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 (CcGAN). 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.
