论文标题
声誉（在）排名系统中的依赖性：人口统计学对输出差异的影响
Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities
论文作者
论文摘要
关于排名系统（RS）的最新文献在排名对象时考虑了用户的曝光率。尽管项目是基于声誉的RS的对象，但用户在此类算法中也具有核心作用。实际上，当对项目进行排名时，用户偏好会通过该用户在平台中的相关性（即其声誉）加权。在本文中，我们制定了不同声誉的概念（DR），并研究是否以敏感属性为特征的用户系统地获得了较低的声誉，从而导致最终排名反映出他们的偏好。我们考虑两个人口属性，即性别和年龄，并表明DR系统地发生。然后，我们提出缓解措施，以确保声誉独立于用户的敏感属性。关于现实世界数据的实验表明，我们的方法可以克服DR并提高排名有效性。
Recent literature on ranking systems (RS) has considered users' exposure when they are the object of the ranking. Although items are the object of reputation-based RS, users have a central role also in this class of algorithms. Indeed, when ranking the items, user preferences are weighted by how relevant this user is in the platform (i.e., their reputation). In this paper, we formulate the concept of disparate reputation (DR) and study if users characterized by sensitive attributes systematically get a lower reputation, leading to a final ranking that reflects less their preferences. We consider two demographic attributes, i.e., gender and age, and show that DR systematically occurs. Then, we propose mitigation, which ensures that reputation is independent of the users' sensitive attributes. Experiments on real-world data show that our approach can overcome DR and also improve ranking effectiveness.
