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：我们引入了机器学习动力课程分配机制。 具体地说, 我们扩展了最先进的课程匹配机制, 使用一个机器学习基础的优惠激励模块。 以迭接、 不同步的方式, 这个模块生成了适合每个学生的对称比较查询。 关于奖励, 我们的机器学习动力课程匹配机制在课程匹配的大特性中保留了有吸引力的策略性。 关于福利, 我们使用一个与真实世界数据相匹配的模拟器进行计算实验。 我们的结果表明, 与课程匹配相比, 刚果解放运动提高了平均学生功用率4%至9%, 最低学生功用率增加了10%至21%, 即使只有10个比较查询。 最后, 我们强调刚果解放运动的实用性, 以及目前大学使用课程匹配的试点容易。</s>
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