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.

入围的任务是将一长串替代方案减少为一组（较小的）最佳或最合适的替代方案。入围名单通常用于奖项提名过程或推荐系统中以显示特色对象。在本文中，我们分析了基于批准数据（一种常见的偏好类型）的入围方法。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，而是由候选名单方法确定的。我们公理地分析已建立的和新的入围方法，并通过基于合成和真实数据的实验评估来补充该分析。我们的结果会根据所需的属性推荐使用哪些入围方法。