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Designing products to meet consumers' preferences is essential for abusiness's success. We propose the Gradient-based Survey (GBS), a discretechoice experiment for multiattribute product design. The experiment elicitsconsumer preferences through a sequence of paired comparisons for partialprofiles. GBS adaptively constructs paired comparison questions based on therespondents' previous choices. Unlike the traditional random utilitymaximization paradigm, GBS is robust to model misspecification by not requiringa parametric utility model. Cross-pollinating the machine learning andexperiment design, GBS is scalable to products with hundreds of attributes andcan design personalized products for heterogeneous consumers. We demonstratethe advantage of GBS in accuracy and sample efficiency compared to the existingparametric and nonparametric methods in simulations.

Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design