Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem

2026-04-23

AI Summary

This study addresses the limited user autonomy in recommender systems tightly coupled with platforms and the unclear multi-stakeholder implications of data portability policies. It presents the first systematic investigation into how user profile portability—within the context of algorithmic pluralism—affects the interests of consumers, content providers, and other stakeholders. The authors develop a simulation framework integrating multi-objective recommendation algorithms with a user data migration model to capture interactive dynamics under varying portability scenarios. Their findings reveal that the impact of data portability on user utility varies significantly across different recommendation algorithms, offering critical insights for designing fair and controllable recommender ecosystems. This work thus provides both policy guidance and technical foundations for advancing equitable data portability mechanisms in algorithmic platforms.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Algorithm Configuration
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

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.

Problem

• data portability
• recommender systems
• algorithmic pluralism
• multi-stakeholder impact
• user modeling

Innovation

• algorithmic pluralism
• profile portability
• recommender ecosystem
• multi-stakeholder fairness
• data portability