Designing products to meet consumers' preferences is essential for a
business's success. We propose the Gradient-based Survey (GBS), a discrete
choice experiment for multiattribute product design. The experiment elicits
consumer preferences through a sequence of paired comparisons for partial
profiles. GBS adaptively constructs paired comparison questions based on the
respondents' previous choices. Unlike the traditional random utility
maximization paradigm, GBS is robust to model misspecification by not requiring
a parametric utility model. Cross-pollinating the machine learning and
experiment design, GBS is scalable to products with hundreds of attributes and
can design personalized products for heterogeneous consumers. We demonstrate
the advantage of GBS in accuracy and sample efficiency compared to the existing
parametric and nonparametric methods in simulations.

通过渐变调查（GBS）实验设计多属性产品以满足消费者偏好，GBS 基于用户之前的选择，无需参数化效用模型，可以应用于具有数百个属性的产品并设计面向异质消费者的个性化产品，模拟结果表明 GBS 在准确性和样本效率方面优于现有的参数化和非参数化方法。