Search papers, labs, and topics across Lattice.
This paper investigates the impact of data portability, enabled by algorithmic pluralism, on user utility and stakeholder outcomes in recommender systems. Through simulations, the study examines how different data portability scenarios affect user utility across various recommendation algorithms. The results show that the effects of data portability vary depending on the algorithm used, highlighting the need for careful policy considerations in designing equitable recommendation ecosystems.
Data portability in recommender systems doesn't guarantee better outcomes for users, as its impact varies significantly depending on the specific recommendation algorithm employed.
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.
