We present RecoWorld, a blueprint for building simulated environments tailored to agentic recommender systems. Such environments give agents a proper training space where they can learn from errors without impacting real users. RecoWorld distinguishes itself with a dual-view architecture: a simulated user and an agentic recommender engage in multi-turn interactions aimed at maximizing user retention. The user simulator reviews recommended items, updates its mindset, and when sensing potential user disengagement, generates reflective instructions. The agentic recommender adapts its recommendations by incorporating these user instructions and reasoning traces, creating a dynamic feedback loop that actively engages users. This process leverages the exceptional reasoning capabilities of modern LLMs. We explore diverse content representations within the simulator, including text-based, multimodal, and semantic ID modeling, and discuss how multi-turn RL enables the recommender to refine its strategies through iterative interactions. RecoWorld also supports multi-agent simulations, allowing creators to simulate the responses of targeted user populations. It marks an important first step toward recommender systems where users and agents collaboratively shape personalized information streams. We envision new interaction paradigms where "user instructs, recommender responds," jointly optimizing user retention and engagement.

翻译：本文提出RecoWorld——一种专为智能推荐系统构建模拟环境的蓝图框架。此类环境为智能体提供了恰当的训练空间，使其能够在学习过程中试错而不影响真实用户。RecoWorld采用双视图架构：模拟用户与智能推荐系统进行多轮交互，以最大化用户留存为目标。用户模拟器对推荐项目进行评价、更新其心智状态，并在感知到用户可能流失时生成反思性指令。智能推荐系统则通过融合这些用户指令与推理轨迹来调整推荐策略，形成动态反馈循环以持续吸引用户。该过程充分利用了现代大语言模型卓越的推理能力。我们在模拟器中探索了多样化的内容表征方式，包括基于文本、多模态及语义ID建模，并讨论了多轮强化学习如何使推荐系统通过迭代交互优化策略。RecoWorld同时支持多智能体仿真，使构建者能够模拟目标用户群体的响应行为。这标志着向用户与智能体协同塑造个性化信息流的推荐系统迈出了重要的第一步。我们展望“用户指令、推荐响应”的新型交互范式，共同优化用户留存与参与度。