Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of these patterns reflect important real-world phenomena driving interactions between the various users and items; other patterns may be irrelevant or reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to one dimension of social concern, namely content creator gender. Using publicly available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms tend to propagate at least some of each user’s tendency to rate or read male or female authors into their resulting recommendations, although they differ in both the strength of this propagation and the variance in the gender balance of the recommendation lists they produce. The data, experimental design, and statistical methods are designed to be reusable for studying potentially discriminatory social dimensions of recommendations in other domains and settings as well.

协作过滤算法可在评级和消费数据中找到有用的模式，并利用这些模式来指导用户购买优质物品。其中许多模式反映了重要的现实世界现象，这些现象推动了各种用户和物品之间的交互。其他模式可能无关紧要或反映了不希望的歧视，例如在出版或购买中对妇女或少数民族作者的歧视。在这项工作中，我们研究了协作过滤推荐器算法对其输入数据的分布的响应，这些响应是关于社会关注的一个维度（即内容创建者性别）的。使用公开可用的图书评分数据，我们可以在用户评分资料和由此产生的推荐列表中衡量图书作者的性别分布。我们发现，常见的协作过滤算法往往会至少传播每个用户对男性或女性作者进行评分或阅读的趋势，并将其纳入推荐的推荐中，尽管这种传播的强度和推荐性别平衡的差异都不同他们产生的清单。数据，实验设计和统计方法被设计为可重复使用，以研究其他领域和环境中推荐的潜在歧视性社会维度。