RecoWorld: Building Simulated Environments for Agentic Recommender Systems

Understand

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.