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.

翻译：短名单是减少一组(较小)最佳或最合适替代物的长长的替代物清单的任务; 短名单经常用于授标提名过程或推荐系统以显示特有物品; 在本文中,我们根据批准数据分析短名单方法,一种常见的偏好类型; 此外,我们假定短名单的大小,即最佳或最合适的替代物的数目,不是固定的,而是由短名单方法决定的; 我们不折不扣地分析既定和新的短名单方法,并以基于合成和现实世界数据的实验性评价作为补充; 我们的结果导致根据预期的特性提出短名单方法的建议。