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.

翻译：设计符合消费者偏好的产品对企业成功至关重要。我们提出梯度调查法，这是一种用于多属性产品设计的离散选择实验。该方法通过一系列针对部分属性的成对比较来获取消费者偏好。梯度调查法根据受访者之前的回答自适应地构建成对比较问题。不同于传统的随机效用最大化范式，梯度调查法无需参数化效用模型，从而对模型设定错误具有鲁棒性。通过交叉融合机器学习与实验设计，梯度调查法可扩展至数百个属性的产品，并为异质性消费者设计个性化产品。在模拟实验中，我们展示了梯度调查法相比现有参数化及非参数化方法在准确性和样本效率上的优势。