论文标题
基于批准的入围名单
Approval-Based Shortlisting
论文作者
论文摘要
入围名单是将一长串替代方案减少到（较小）最佳或最合适的替代方案的任务。候选名单通常用于提名奖项的提名过程或推荐系统中以显示特色对象。在本文中，我们分析基于批准数据的入围方法，这是一种常见的偏好类型。此外，我们假设候选名单的大小，即最佳或最合适的替代方案的数量，不是固定的，而是由入围方法确定的。我们公理地分析了已建立的新候选方法，并通过基于合成和现实世界数据的实验评估来补充这种分析。我们的结果导致建议使用哪种入围方法，具体取决于所需的属性。
Shortlisting is the task of reducing a long list of alternatives to a (smaller) set of best or most suitable alternatives. Shortlisting is often used in the nomination process of awards or in recommender systems to display featured objects. In this paper, we analyze shortlisting methods that are based on approval data, a common type of preferences. Furthermore, we assume that the size of the shortlist, i.e., the number of best or most suitable alternatives, is not fixed but determined by the shortlisting method. We axiomatically analyze established and new shortlisting methods and complement this analysis with an experimental evaluation based on synthetic and real-world data. Our results lead to recommendations which shortlisting methods to use, depending on the desired properties.
