Anas Buhayh

Anas Buhayh, Elizabeth McKinnie, Clement Canel et al.

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.

Anas Buhayh, Elizabeth McKinnie, Robin Burke

Recommender ecosystems are an emerging subject of research. Such research examines how the characteristics of algorithms, recommendation consumers, and item providers influence system dynamics and long-term outcomes. One architectural possibility that has not yet been widely explored in this line of research is the consequences of a configuration in which recommendation algorithms are decoupled from the platforms they serve. This is sometimes called "the friendly neighborhood algorithm store" or "middleware" model. We are particularly interested in how such architectures might offer a range of different distributions of utility across consumers, providers, and recommendation platforms. In this paper, we create a model of a recommendation ecosystem that incorporates algorithm choice and examine the outcomes of such a design.

Elizabeth McKinnie, Anas Buhayh, Clement Canel et al.

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.