让用户自由切换推荐算法，能更好保护小众用户和内容创作者的公平性。
Fairness for niche users and providers: algorithmic choice and profile portability
- 通过解耦平台与推荐算法，让用户自主选择算法。
- 模拟显示小众用户和内容提供者在算法可选时受益更多。
- 研究算法多样性对公平性的提升，适合关注推荐系统伦理的研究者。
在推荐系统中，公平性通常通过算法干预实现：构建更具公平性的新模型，或通过重排序改进现有算法的结果。然而，推荐生态系统的结构性变革却很少被研究。本文探讨算法多元主义（algorithmic pluralism）的公平性影响，即推荐算法与平台解耦，使用户能够自主选择算法。先前的仿真研究表明，小众消费者和（尤其是）小众内容提供者从算法选择中获益。本文进一步利用仿真分析用户资料可迁移性（profile portability）的不同政策如何与消费者和提供者的公平性结果相互作用。
原文摘要 · Abstract (English)
Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an existing algorithm. What has rarely been studied is structural changes in the recommendation ecosystem itself. Our work explores the fairness impact of algorithmic pluralism, the idea that the recommendation algorithm is decoupled from the platform through which users access content, enabling user choice in algorithms. Prior work using a simulation approach has shown that niche consumers and (especially) niche providers benefit from algorithmic choice. In this paper, we use simulation to explore the question of profile portability, to understand how different policies regarding the handling of user profiles interact with fairness outcomes for consumers and providers.
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