Technical Note—Active Learning for Nonparametric Choice Models

Understanding consumer preferences is crucial for designing better products, optimizing assortments, and personalizing recommendations. In “Active Learning for Nonparametric Choice Models,” Fransisca Susan, Negin Golrezaei, Ehsan Emamjomeh-Zadeh, and David Kempe present an active learning approach that efficiently uncovers the most informative patterns in the choices made by members of a heterogeneous population. Instead of relying solely on historical transaction data, their method strategically selects sets of products to offer and then uses the responses to construct a directed acyclic graph (DAG) representation of preferences. This DAG captures the top k choices, their probabilities, and how they relate to each other as k changes. Experiments on synthetic and real‐world data show that the method learns preferences more accurately—and with fewer data points—than leading offline techniques. This work advances both the theory and practice of preference learning, with implications for retail, online platforms, and artificial intelligence agents that need to model human decision making.