Estimating Large-Scale Tree Logit Models
针对树形Logit模型（也称嵌套Logit模型）在大规模产品场景下难以估计的问题，提出一种快速迭代算法，利用负对数似然的结构进行简单闭式更新，数值实验表明该方法优于现有优化方法。
In “Estimating Large-Scale Tree Logit Models,” S. Jagabathula, P. Rusmevichientong, A. Venkataraman, and X. Zhao tackle the demand estimation problem under the tree logit model, also known as the nested logit or d-level nested logit model. The model is ideal for scenarios in which products can be grouped naturally based on their attributes into a hierarchy or taxonomy, such as flight itineraries grouped by departure time (morning or evening) and number of stops (nonstop or one stop). The current estimation methods are not practical for real-world applications that can involve hundreds or even thousands of products. The authors develop a fast, iterative method that computes a sequence of parameter estimates using simple closed-form updates by exploiting the structure of the negative log-likelihood objective. Numerical results on both synthetic and real data show that their proposed algorithm outperforms state-of-the-art optimization methods, especially for large-scale tree logit models with thousands of products.