Adaptive Preference Measurement with Unstructured Data

Abstract

Many products are most meaningfully described using unstructured data like text or images. Unstructured data are also common in e-commerce, where products are often described by photos and text, but not with standardized sets of attributes. While much is known about how to efficiently measure consumer preferences when products can be meaningfully described by structured attributes, there is scant research on doing the same for unstructured data. This paper introduces a real-time, adaptive survey design framework for measuring preferences over unstructured data, leveraging Bayesian optimization. By adaptively choosing items to display based on uncertainty around a nonparametric utility model, the proposed method maximizes information gain per question, enabling quick estimation of individual-level preferences. The approach operates on embeddings of the unstructured data, thereby eliminating the requirement for manual coding of product attributes. We apply the method to measuring preferences over clothing, and highlight its potential both for the general task of marketing research, and for the specific task of designing customer onboarding surveys to mitigate the cold-start recommendation problem. We also develop methods for interpreting the nonparametric utility functions, which allow us to reconstruct consumer valuations of discrete attributes, even for attributes that were not considered or available a priori.

Keywords: preference measurement, Bayesian optimization, representation learning, recommendation systems, image data, conjoint analysis