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 through simulations 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. Code available: https://github.com/epfl-dlab/amplification_paradox.

翻译：对推荐系统的自动化审计发现，盲目遵循推荐会导致用户接触到日益偏激、阴谋论或虚假的内容。与此同时，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，这些内容大多通过其他途径（如其他网站）被获取。本文解释了一个明显的悖论：如果推荐算法偏好极端内容，为何它并未驱动其消费？通过一个简单的基于智能体的模型（该模型中用户对推荐系统内的物品赋予不同的效用），我们通过模拟证明，推荐系统的协同过滤特性与极端内容的 niche 性（小众性）可以化解这一表面悖论：虽然盲目遵循推荐确实会将用户引向 niche 内容，但当用户有选择权时，他们很少消费这类内容，因为对其效用较低，这可能导致推荐系统对这类内容进行去放大。我们的结果呼吁对“算法放大”进行更细致的解读，并强调在审计推荐系统时，需要对用户对内容的效用进行建模。代码地址：https://github.com/epfl-dlab/amplification_paradox。