Optimizing outcomes for multiple stakeholders in recommender systems has historically focused on algorithmic interventions, such as developing multi-objective models or re-ranking results from existing algorithms. However, structural changes to the recommendation ecosystem itself remain understudied. This paper explores the implications of algorithmic pluralism (also known as "middleware" in the governance literature), in which recommendation algorithms are decoupled from platforms, enabling users to select their preferred algorithm. Prior simulation work demonstrates that algorithmic choice benefits niche consumers and providers. Yet this approach raises critical questions about user modeling in the context of data portability: when users switch algorithms, what happens to their data? Noting that multiple data portability regulations have emerged to strengthen user data ownership and control. We examine how such policies affect user models and stakeholders' outcomes in recommendation setting. Our findings reveal that data portability scenarios produce varying effects on user utility across different recommendation algorithms. We highlight key policy considerations and implications for designing equitable recommendation ecosystems.

翻译：优化推荐系统中多方利益相关者的结果历来侧重于算法干预，例如开发多目标模型或对现有算法的结果进行重新排序。然而，对推荐系统本身结构性变化的研究仍显不足。本文探讨了算法多元主义（在治理文献中也被称为“中间件”）的影响，即推荐算法与平台解耦，使用户能够选择其偏好的算法。先前的模拟研究表明，算法选择有利于小众消费者和服务提供者。然而，这种方法引发了关于数据可移植性背景下用户建模的关键问题：当用户切换算法时，他们的数据会发生什么？注意到已有多个数据可移植性法规出台以加强用户数据所有权和控制权，我们研究了此类政策如何影响推荐场景中的用户模型和利益相关者结果。我们的研究结果表明，数据可移植性场景在不同推荐算法下对用户效用产生差异化影响。我们强调了构建公平推荐生态系统的关键政策考量及其启示。