Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data
<|reference_start|>Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data: 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.<|reference_end|>
@article{li2024product,
    title={Product Design Using Generative Adversarial Network: Incorporating
  Consumer Preference and External Data},
    author={Hui Li, Jian Ni and Fangzhu Yang},
    journal={arXiv preprint arXiv:2405.15929},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15929},
    primaryClass={econ.GN cs.HC q-fin.EC}
}