Social choice theory provides a principled framework for the aggregation of individuals' preferences in support of group decision‐making and recommendation. Much of this work, however, either assumes that individuals' subjective preferences (and thus, their votes) are correctly specified by the individuals themselves, or alternatively that the votes of individuals are noisy estimates of some underlying ground truth over rankings of alternatives. We argue that neither model appropriately addresses some of the issues which arise in the context of group‐recommendation domains where individuals have subjective preferences but for some reason (eg, the high cognitive burden, concerns about privacy, etc.) may instead vote using a noisy estimate of their subjective preference rankings. In this paper, we propose a general probabilistic framework for modeling noisy subjective preferences, and explore the accuracy and reliability of four well‐studied voting rules under various noise models. Our results demonstrate that there is no single reliable method amongst the examined methods. Specifically, we observe the change in noise distribution can flip one method from being the most reliable to the least.

中文翻译：

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具有嘈杂主观偏好的小组推荐

社会选择理论为汇总个人偏好提供了原则性框架，以支持群体决策和推荐。但是，许多工作要么假设个人的主观偏好（以及他们的投票）是由他们自己正确指定的，要么是假设个人的投票是对替代方案排名的某些基本事实的嘈杂估计。我们认为，这两种模型都无法恰当地解决在个人推荐具有主观偏好但出于某些原因（例如，高认知负担，对隐私的担忧等）的群体推荐领域中出现的一些问题，而可能会改为使用他们的主观偏好排名的嘈杂估计。在本文中，我们提出了一个用于对嘈杂的主观偏好进行建模的通用概率框架，并探讨了在各种噪声模型下四个经过充分研究的投票规则的准确性和可靠性。我们的结果表明，在所检查的方法中没有单一的可靠方法。具体来说，我们观察到噪声分布的变化可使一种方法从最可靠的方法翻转到最小的方法。