Algorithmic recommendation based on noisy preference measurement is prevalent in recommendation systems. This paper discusses the consequences of such recommendation on market concentration and inequality. Binary types denoting a statistical majority and minority are noisily revealed through a statistical experiment. The achievable utilities and recommendation shares for the two groups can be analyzed as a Bayesian Persuasion problem. While under arbitrary noise structures, effects on concentration compared to a full-information market are ambiguous, under symmetric noise, concentration increases and consumer welfare becomes more unequal. We define symmetric statistical experiments and analyze persuasion under a restriction to such experiments, which may be of independent interest.

翻译：基于含噪偏好测量的算法推荐在推荐系统中普遍存在。本文探讨了此类推荐对市场集中度与不平等性的影响。通过统计实验，以含噪方式揭示代表统计多数群体与少数群体的二元类型。两组群体可实现的效用与推荐份额可被建模为一个贝叶斯说服问题进行分析。尽管在任意噪声结构下，与完全信息市场相比，其对集中度的影响并不明确；但在对称噪声条件下，集中度会上升，消费者福利将变得更为不平等。我们定义了对称统计实验，并分析了限定在此类实验下的说服问题，该分析本身可能具有独立的研究价值。