LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries

2026-05-10

AI Summary

This work addresses the limitations of existing personalized services, which are typically confined to single platforms and unable to effectively integrate heterogeneous user data across multiple online platforms and offline contexts, resulting in incomplete user profiles. To overcome this challenge, the paper introduces a novel user-centric paradigm for cross-domain personalization that places the user at the center of the process. By leveraging readily available large language model (LLM) agents, the proposed approach intelligently reasons over and fuses multi-source data voluntarily exported by users themselves, thereby breaking down platform-specific data silos. Experimental results demonstrate that this method significantly outperforms baseline approaches relying solely on single-platform data, confirming the effectiveness and superiority of a user-controlled, boundary-spanning personalization system.

Abstract

Personalization today is fundamentally platform-centric: services build user representations from the behavioral fragments they observe. Yet no platform can construct a complete picture of the user, as competitive incentives, legal constraints, user privacy concerns, and epistemic limits create persistent data barriers. This paper argues for a shift from platform-centric personalization to user-governed personalization, where only the user can integrate fragmented contexts across platforms and the offline world. The key asymmetry lies in data access: only users can aggregate their own cross-platform and offline information. Large language model (LLM) agents make such integration practically feasible for the first time by enabling reasoning over heterogeneous personal data and transforming users' cross-context information into actionable personalization capabilities. We provide proof-of-concept evidence that users equipped with cross-platform data exports and an off-the-shelf LLM agent can outperform single-platform personalization baselines. We conclude by outlining a research agenda for building scalable user-governed personalization systems.

Problem

• personalization
• data fragmentation
• platform boundaries
• user governance
• cross-platform integration

Innovation

• user-governed personalization
• LLM agents
• cross-platform data integration
• heterogeneous personal data
• personalization beyond platforms