论文标题
机器学习驱动的课程分配
Machine Learning-Powered Course Allocation
论文作者
论文摘要
我们研究课程分配问题，大学为学生分配课程时间表。当前的最新机制课程匹配有一个主要的缺点：学生在报告偏好时犯了重大错误，这会对福利和公平产生负面影响。为了解决这个问题，我们介绍了一种新机制，机器学习驱动的课程匹配（MLCM）。 MLCM的核心是机器学习驱动的偏好启发模块，它迭代地询问个性化的成对比较查询，以减轻学生的报告错误。基于现实世界数据的广泛计算实验表明，只有十个比较查询的MLCM将平均和最小学生公用事业分别显着增加7％-11％和17％-29％。最后，我们重点介绍了MLCM对环境变化的鲁棒性，并显示我们的设计如何最大程度地降低了升级到MLCM的风险，同时使大学简单地为学生提供了简单的升级过程。
We study the course allocation problem, where universities assign course schedules to students. The current state-of-the-art mechanism, Course Match, has one major shortcoming: students make significant mistakes when reporting their preferences, which negatively affects welfare and fairness. To address this issue, we introduce a new mechanism, Machine Learning-powered Course Match (MLCM). At the core of MLCM is a machine learning-powered preference elicitation module that iteratively asks personalized pairwise comparison queries to alleviate students' reporting mistakes. Extensive computational experiments, grounded in real-world data, demonstrate that MLCM, with only ten comparison queries, significantly increases both average and minimum student utility by 7%-11% and 17%-29%, respectively. Finally, we highlight MLCM's robustness to changes in the environment and show how our design minimizes the risk of upgrading to MLCM while making the upgrade process simple for universities and seamless for their students.
