标题： 半监督学习用于离散选择模型 显示英文标题

标题： Semi-supervised Learning for Discrete Choice Models

摘要： 我们引入了一个半监督的离散选择模型，当相对较少的请求同时具有选择集和声明偏好，但大多数仅具有选择集时，用于校准离散选择模型。 两种经典的半监督学习算法，期望最大化算法和聚类与标记算法，已被适应到我们的选择建模问题设置中。 我们还基于聚类与标记算法开发了两种新算法。 新算法使用贝叶斯信息准则来评估聚类设置，以自动调整聚类数量。 展示了两项计算研究，包括一个酒店预订案例和一个大规模航空公司行程购物案例，以评估所提出算法的预测准确性和计算工作量。 在各种场景下给出了算法建议。 显示英文摘要

摘要： We introduce a semi-supervised discrete choice model to calibrate discrete choice models when relatively few requests have both choice sets and stated preferences but the majority only have the choice sets. Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the cluster-and-label algorithm, have been adapted to our choice modeling problem setting. We also develop two new algorithms based on the cluster-and-label algorithm. The new algorithms use the Bayesian Information Criterion to evaluate a clustering setting to automatically adjust the number of clusters. Two computational studies including a hotel booking case and a large-scale airline itinerary shopping case are presented to evaluate the prediction accuracy and computational effort of the proposed algorithms. Algorithmic recommendations are rendered under various scenarios.