Personalization has traditionally depended on platform-specific user models that are optimized for prediction but remain largely inaccessible to the people they describe. As LLM-based assistants increasingly mediate search, shopping, travel, and content access, this arrangement may be giving way to a new personalization stack in which user representation is no longer confined to isolated platforms. In this paper, we argue that the key issue is not simply that large language models can enhance recommendation quality, but that they reconfigure where and how user representations are produced, exposed, and acted upon. We propose a shift from hidden platform profiling toward governable personalization, where user representations may become more inspectable, revisable, portable, and consequential across services. Building on this view, we identify five research fronts for recommender systems: transparent yet privacy-preserving user modeling, intent translation and alignment, cross-domain representation and memory design, trustworthy commercialization in assistant-mediated environments, and operational mechanisms for ownership, access, and accountability. We position these not as isolated technical challenges, but as interconnected design problems created by the emergence of LLM agents as intermediaries between users and digital platforms. We argue that the future of recommender systems will depend not only on better inference, but on building personalization systems that users can meaningfully understand, shape, and govern.

翻译：个性化推荐传统上依赖于为预测优化但对其描述对象基本不可见的平台专属用户模型。随着基于大语言模型的助手越来越多地介入搜索、购物、旅行和内容访问等场景，这种模式可能正在被一种新的个性化技术栈所取代：用户表征不再局限于孤立的平台。本文认为，核心问题不仅在于大语言模型能够提升推荐质量，更在于它们重构了用户表征的产生、呈现和调用方式。我们主张从隐藏的平台画像转向可治理的个性化，使得用户表征在跨服务场景中变得更加可审查、可修改、可移植且更具影响力。基于这一视角，我们识别出推荐系统面临的五大研究前沿：透明且隐私保护的用户建模、意图翻译与对齐、跨域表征与记忆设计、助手中介环境下的可信商业化，以及所有权、访问权限与问责机制的运行方案。我们将其定位为由大语言模型代理作为用户与数字平台中介的崛起所引发的相互关联的设计问题，而非孤立的技术挑战。我们认为推荐系统的未来不仅取决于更强大的推断能力，更在于构建用户能够真正理解、塑造和治理的个性化系统。