Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating through the available options, there have been growing concerns regarding its repercussions on the users and their opinions. Examples of negative impacts include the emergence of filter bubbles and the amplification of users' confirmation bias, which can cause opinion polarization and radicalization. In this paper, we study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic (i.e., at the level of a homogenous population) perspective. Specifically, we build on recent work on the interactions between opinion dynamics and recommendation systems to propose a model for this closed loop, which we then study both analytically and numerically. Among others, our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population. In particular, even in settings where the opinion distribution appears unaltered (e.g., measured via surveys across the population), the opinion of individual users might be significantly distorted by the recommendation system.

翻译：推荐系统被广泛应用于社交网络和电子商务平台等网络服务中，通过向用户提供个性化内容来增强其体验。尽管个性化推荐有助于用户在海量选项中进行导航，但人们日益担忧其对用户及其观点的影响。负面影响包括信息茧房的出现以及用户确认偏见的加剧，这些可能导致观点极化与激进主义。本文从微观（即个体用户层面）和宏观（即同质化群体层面）两个视角，研究推荐系统对用户的影响。具体而言，我们基于近期关于观点动态与推荐系统交互作用的研究，提出这一闭环系统的模型，并从解析与数值两方面进行探究。分析结果表明，个体用户观点的变化并不总是与群体观点分布的变化保持一致。特别地，即使当观点分布看似未改变时（例如通过群体调查测得的分布），个体用户的观点也可能因推荐系统而显著扭曲。