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.

翻译：当前的个性化服务本质上是平台中心化的：各平台从其观测到的行为片段构建用户表征。然而，由于竞争激励、法律约束、用户隐私关切以及认知局限等因素形成了持续的数据壁垒，没有任何平台能构建用户的完整画像。本文主张从平台中心化个性化向用户自主个性化转变——唯有用户能够整合跨平台及线下世界的碎片化情境。其核心不对称性在于数据访问权限：只有用户自身才能聚合跨平台与线下的信息。大语言模型智能体通过支持异构个人数据推理，并可将用户跨情境信息转化为可操作的个性化能力，从而首次使这种整合具备实践可行性。我们提供的概念验证表明，配备跨平台数据导出工具与现成大语言模型智能体的用户，能够超越单一平台的个性化基线。最后，我们提出构建可扩展用户自主个性化系统的研究议程。