Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data (2405.15929v3)

Abstract: The rise of generative AI has facilitated automated product design but often neglects valuable consumer preference data within companies' internal datasets. Additionally, external sources such as social media and user-generated content (UGC) platforms contain substantial untapped information on product design and consumer preferences, yet remain underutilized. We propose a novel framework that transforms the product design paradigm to be data-driven, automated, and consumer-centric. Our method employs a semi-supervised deep generative architecture that systematically integrates multidimensional consumer preferences and heterogeneous external data. The framework is both generative and preference-aware, enabling companies to produce consumer-aligned designs with enhanced cost efficiency. Our framework trains a specialized predictor model to comprehend consumer preferences and utilizes predicted popularity metrics to guide a continuous conditional generative adversarial network (CcGAN). The trained CcGAN can directionally generate consumer-preferred designs, circumventing the expenditure associated with testing suboptimal candidates. Using external data, our framework offers particular advantages for start-ups or other resource-constrained companies confronting the ``cold-start" problem. We demonstrate the framework's efficacy through an empirical application with a self-operated photography chain, where our model successfully generated superior photo template designs. We also conduct web-based experiments to verify our method and confirm its effectiveness across varying design contexts.