The technical foundations of recommender systems have progressed from collaborative filtering to complex neural models and, more recently, large language models. Despite these technological advances, deployed systems often underserve their users by simply presenting a list of items, leaving the burden of exploration, comparison, and synthesis entirely on the user. This paper argues that this traditional "tool-based" paradigm fundamentally limits user experience, as the system acts as a passive filter rather than an active assistant. To address this limitation, we propose a novel deep research paradigm for recommendation, which replaces conventional item lists with comprehensive, user-centric reports. We instantiate this paradigm through RecPilot, a multi-agent framework comprising two core components: a user trajectory simulation agent that autonomously explores the item space, and a self-evolving report generation agent that synthesizes the findings into a coherent, interpretable report tailored to support user decisions. This approach reframes recommendation as a proactive, agent-driven service. Extensive experiments on public datasets demonstrate that RecPilot not only achieves strong performance in modeling user behaviors but also generates highly persuasive reports that substantially reduce user effort in item evaluation, validating the potential of this new interaction paradigm.

翻译：推荐系统的技术基础已从协同过滤发展到复杂的神经模型，再到最近的大型语言模型。尽管技术不断进步，但现有部署系统通常仅向用户呈现项目列表，将探索、比较与综合的负担完全置于用户身上，从而未能充分满足用户需求。本文认为，这种传统的“工具型”范式从根本上限制了用户体验，因为系统仅充当被动过滤器而非主动助手。为突破这一局限，我们提出一种新颖的推荐系统深度研究范式，该范式以全面、以用户为中心的报告取代传统的项目列表。我们通过RecPilot框架具体实现这一范式，该多智能体框架包含两个核心组件：自主探索项目空间的用户轨迹模拟智能体，以及将探索结果合成为连贯、可解释、支持用户决策的自我演进报告生成智能体。该方法将推荐重新定义为一种主动的、智能体驱动的服务。在公开数据集上的大量实验表明，RecPilot不仅在用户行为建模方面表现出色，还能生成极具说服力的报告，显著降低用户在项目评估中的认知负担，验证了这一新型交互范式的潜力。