Technical Note—Active Learning for Nonparametric Choice Models
提出一种主动学习方法，通过策略性地选择产品组合并构建有向无环图来高效揭示异质人群的偏好模式，比传统离线方法用更少数据更准确地学习偏好。
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.