优化排名聚合的位置评分规则
Optimizing positional scoring rules for rank aggregation
摘要：
如今，一些众包项目利用社会选择方法来计算工人提供的个人排名的替代品的总排名。出于这样的系统，我们考虑设置每个工人被要求排名固定（小）数量的替代品，然后，位置评分规则被用来计算的总排名。在这些看似无穷无尽的规则中，哪一条是最好的？为了回答这个问题，我们假设我们可以部分访问潜在的真实排名。然后，要解决的重要优化问题是计算位置评分规则，当应用于个人排名的概况时，其结果尽可能接近我们所知道的潜在真实排名的部分。我们从理论的角度研究这个基本问题，并提出积极和消极的复杂性结果，此外，补充我们的理论研究结果与现实世界和合成数据的实验。
英文摘要：
Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers. Motivated by such systems, we consider a setting where each worker is asked to rank a fixed (small) number of alternatives and, then, a positional scoring rule is used to compute the aggregate ranking. Among the apparently infinite such rules, what is the best one to use? To answer this question, we assume that we have partial access to an underlying true ranking. Then, the important optimization problem to be solved is to compute the positional scoring rule whose outcome, when applied to the profile of individual rankings, is as close as possible to the part of the underlying true ranking we know. We study this fundamental problem from a theoretical viewpoint and present positive and negative complexity results and, furthermore, complement our theoretical findings with experiments on real-world and synthetic data.
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