AI Insight
This paper introduces SR-Agent, an automated framework that refines post-ranking strategies in e-commerce recommendation systems without manual intervention. The system uses three AI agents working in sequence: one identifies problematic user experiences, another diagnoses recurring issues, and a third implements controlled strategy improvements with safety checks. Deployed on Kuaishou's e-commerce platform for one month, SR-Agent increased order volume by 0.71%, browsing depth by 0.34%, and category diversity by 0.48% while reducing operational costs.
Why it matters
This represents the first industrial deployment of a fully automated, self-evolving system for optimizing recommendation strategies in e-commerce. The framework addresses a significant operational bottleneck where manual strategy updates are slow and costly, potentially enabling more adaptive and user-responsive recommendation systems at scale.
Understand the Science
arXiv:2607.17719v2 Announce Type: replace
Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost. However, as the online recommendation environment evolves continuously, these statically configured strategies gradually become stale, thereby degrading the user experience. Refining them typically relies on manual inspection, diagnosis, and updates, making it slow, costly, and difficult to scale or reuse. Although recent LLM-based agents (e.g., RecUserSim, SimUSER, and Self-EvolveRec) offer promising directions, none of them close the full loop of automated, self-evolving strategy refinement. To bridge this gap, we introduce SR-Agent, which, to the best of our knowledge, is the first agentic framework deployed to refine post-ranking strategies in industrial RS. SR-Agent unifies three components: (i) a UserSim agent that applies inspection skills to surface user-perceived bad cases; (ii) an Analysis agent that consolidates recurring bad cases into structured, reusable diagnoses; and (iii) a constrained Strategy Refinement Harness that maps diagnoses to typed and bounded actions, gated by a four-stage reward pipeline with reversible rollback. Deployed on the Kuaishou e-commerce platform, SR-Agent continuously runs this refinement loop and, in a one-month online A/B test, increases order volume by 0.71%, browsing depth by 0.34%, and clicked-category diversity by 0.48%, while markedly shortening the refinement cycle and lowering operational cost.