LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
Jiacheng Lin, Kun Qian, Arvind Srinivasan, Tian Wang, Fang Han + 13 more
TLDR
LLM agents empower users to integrate and govern their personal data across platforms, moving beyond fragmented, platform-centric personalization.
Key contributions
- Proposes user-governed personalization, shifting from fragmented platform-centric models.
- LLM agents integrate diverse personal data across platforms and offline contexts.
- Proof-of-concept shows LLM agents outperform single-platform personalization baselines.
Why it matters
This paper introduces a paradigm shift in personalization, empowering users to control their data across platforms. By leveraging LLM agents, it offers a path towards more comprehensive and privacy-respecting personalization, overcoming current data silos.
Original 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.
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