用大模型代理让用户自主整合跨平台数据，实现更全面的个性化。
LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
- 用户通过大模型代理整合跨平台和线下数据，实现自主个性化。
- 实验表明，用户用跨平台数据+现成大模型，效果优于单一平台方案。
- 适合关注隐私保护与个性化平衡的研究者和产品设计者。
当前个性化服务依赖平台收集用户行为片段构建用户画像，但受竞争、法律、隐私和认知限制，单个平台无法掌握完整用户信息。本文提出从平台中心化转向用户主导的个性化模式，强调只有用户能聚合跨平台及线下数据。大语言模型（LLM）代理首次使这种整合成为可能，通过推理异构个人数据，将多源信息转化为可操作的个性化能力。我们提供了初步证据：拥有跨平台数据导出和现成LLM代理的用户，其个性化表现优于单一平台基线。最后，本文提出了构建可扩展用户主导个性化系统的研究方向。
原文摘要 · Abstract (English)
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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