Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content. At the same time, studies using real user traces suggest that recommender systems are not the primary driver of attention toward extreme content; on the contrary, such content is mostly reached through other means, e.g., other websites. In this paper, we explain the following apparent paradox: if the recommendation algorithm favors extreme content, why is it not driving its consumption? With a simple agent-based model where users attribute different utilities to items in the recommender system, we show that the collaborative-filtering nature of recommender systems and the nicheness of extreme content can resolve the apparent paradox: although blindly following recommendations would indeed lead users to niche content, users rarely consume niche content when given the option because it is of low utility to them, which can lead the recommender system to deamplify such content. Our results call for a nuanced interpretation of ``algorithmic amplification'' and highlight the importance of modeling the utility of content to users when auditing recommender systems.

翻译：对推荐系统的自动审计发现，盲目遵循推荐会导致用户日益倾向于极端、阴谋论或虚假内容。然而，基于真实用户轨迹的研究表明，推荐系统并非用户接触极端内容的主要驱动力；相反，这类内容大多通过其他途径（如其他网站）被访问。在本文中，我们解释以下表面上的悖论：如果推荐算法偏向极端内容，为何它并未推动其消费？通过一个简单的基于智能体的模型（其中用户对推荐系统中各条内容赋予不同的效用值），我们证明推荐系统的协同过滤特性与极端内容的冷门性可以化解这一表面悖论：尽管盲目遵循推荐确实会将用户引向冷门内容，但当用户拥有选择权时，他们很少消费冷门内容——因其对他们效用较低，这可能导致推荐系统对此类内容产生去放大效应。我们的研究呼吁对“算法放大”进行更细致的解读，并强调在审计推荐系统时对用户内容效用建模的重要性。