KDD Papers

Deep Design: Product Aesthetics for Heterogeneous Markets

Yanxin Pan (University of Michigan);Alexander Burnap (University of Michigan);Jeffrey Hartley (General Motors);Richard Gonzalez (University of Michigan);Panos Papalambros (University of Michigan)


Aesthetic appeal is a primary driver of customer consideration over product designs such as automobiles. Product designers must accordingly convey design attributes (e.g., `Sportiness’) that the customer will prefer, a challenging proposition given subjective perceptions of customers belonging to heterogeneous market segments. We introduce a scalable deep learning approach that aims to predict how customers across market segments perceive aesthetic designs, as well as visually interpret ``why” the customer perceives as such. An experiment is conducted to test this approach, using a Siamese neural network architecture containing a pair of conditional generative adversarial networks, trained using large-scale product design and crowdsourced customer data. Our results show that we are able to predict how aesthetic design attributes are perceived by customers in heterogeneous market segments, as well visually interpret these aesthetic perceptions. This provides evidence that the proposed deep learning approach may provide an additional means of understanding customer aesthetic perceptions complementary to existing methods used in product design.