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：推荐系统广泛应用于网络服务（如社交网络和电子商务平台），通过向用户提供个性化内容来提升其体验。尽管个性化能帮助用户在众多选项中进行导航，但其对用户及其观点产生的潜在影响日益引发担忧。负面影响的典型案例包括过滤气泡的出现及用户确认偏见的放大，这些现象可能导致观点极化与极端化。本文从微观（即个体用户层面）与宏观（即同质群体层面）两个视角，系统研究了推荐系统对用户的影响。具体而言，我们基于观点动态与推荐系统交互作用的最新研究成果，提出一个描述该闭环系统的模型，并进行了解析分析与数值模拟。研究结果揭示：个体用户观点的偏移与群体观点分布的偏移并非总是一致。特别值得注意的是，即便在群体观点分布看似未发生变化（例如通过群体范围内的调查测量）的情境下，推荐系统仍可能对个体用户的观点产生显著扭曲效应。