RecoWorld: Building Simulated Environments for Agentic Recommender
  Systems
Authors: name: Fei Liu, name: Xinyu Lin, name: Hanchao Yu, name: Mingyuan Wu, name: Jianyu Wang, name: Qiang Zhang, name: Zhuokai Zhao, name: Yinglong Xia, name: Yao Zhang, name: Weiwei Li, name: Mingze Gao, name: Qifan Wang, name: Lizhu Zhang, name: Benyu Zhang, name: Xiangjun Fan

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.