RecoWorld: Building Simulated Environments for Agentic Recommender
  Systems
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.
68c814fef1d159e090f25e3c
Fei Liu
claimed_verified
2025-09-15T15:04:43.754Z
68c2f53e1dcca208f5a5af67
/avatars/ecf0fa3be24d7e90421471b31fcc0c7d.svg
Fei Liu
user
feiliu1
68c814fef1d159e090f25e3d
Xinyu Lin
68c814fef1d159e090f25e3e
Hanchao Yu
68c814fef1d159e090f25e3f
Mingyuan Wu
68c814fef1d159e090f25e40
Jianyu Wang
68c814fef1d159e090f25e41
Qiang Zhang
68c814fef1d159e090f25e42
Zhuokai Zhao
68c814fef1d159e090f25e43
Yinglong Xia
68c814fef1d159e090f25e44
Yao Zhang
68c814fef1d159e090f25e45
Weiwei Li
claimed_verified
2025-09-19T06:50:32.831Z
67acc60fe68b373f3ef6e6a7
/avatars/941789c472939e0c08e3e62356e6b278.svg
Weiwei Li
user
VilockLi
68c814fef1d159e090f25e46
Mingze Gao
68c814fef1d159e090f25e47
Qifan Wang
68c814fef1d159e090f25e48
Lizhu Zhang
68c814fef1d159e090f25e49
Benyu Zhang
68c814fef1d159e090f25e4a
Xiangjun Fan
RecoWorld: Building Simulated Environments for Agentic Recommender
  Systems
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.
RecoWorld is a simulated environment for agentic recommender systems that uses a dual-view architecture with user and recommender interactions, leveraging LLMs and multi-turn RL to enhance user retention and engagement.
agentic recommender systems dual-view architecture user simulator multi-turn interactions user retention reflective instructions reasoning traces modern LLMs content representations text-based multimodal semantic ID modeling multi-turn RL multi-agent simulations personalized information streams