Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem

TL;DR

This paper investigates how data portability policies and algorithmic choice impact user utility and stakeholder outcomes in recommender systems, emphasizing structural ecosystem changes beyond algorithm design.

Contribution

It analyzes the effects of algorithmic pluralism and data portability regulations on user models and stakeholder benefits in recommendation ecosystems.

Findings

Data portability scenarios affect user utility differently across algorithms.

Algorithmic choice benefits niche consumers and providers.

Policy considerations are crucial for equitable recommendation ecosystems.

Abstract

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.