We introduce a machine learning-powered course allocation mechanism. Concretely, we extend the state-of-the-art Course Match mechanism with a machine learning-based preference elicitation module. In an iterative, asynchronous manner, this module generates pairwise comparison queries that are tailored to each individual student. Regarding incentives, our machine learning-powered course match (MLCM) mechanism retains the attractive strategyproofness in the large property of Course Match. Regarding welfare, we perform computational experiments using a simulator that was fitted to real-world data. Our results show that, compared to Course Match, MLCM increases average student utility by 4%-9% and minimum student utility by 10%-21%, even with only ten comparison queries. Finally, we highlight the practicability of MLCM and the ease of piloting it for universities currently using Course Match.

翻译：我们提出了一种基于机器学习的课程分配机制。具体而言，我们在最先进的Course Match机制基础上，扩展了一个基于机器学习的偏好引导模块。该模块以迭代、异步的方式生成针对每个学生个性化的两两比较查询。在激励方面，我们的机器学习增强型课程匹配（MLCM）机制保留了Course Match所具备的吸引人的大规模策略鲁棒性。在福利方面，我们利用基于真实数据拟合的模拟器进行了计算实验。结果表明，与Course Match相比，即使仅使用十次比较查询，MLCM也能将学生平均效用提升4%-9%，最低效用提升10%-21%。最后，我们强调了MLCM的实用性以及当前使用Course Match的大学进行试点的便捷性。