Research Paper

Core Innovation

This paper introduces SR-Agent, the first deployed agentic framework that closes the loop on automated, self-evolving refinement of post-ranking strategies in industrial recommender systems. It integrates user simulation, structured diagnosis, and constrained strategy updates with rollback, enabling continuous, experience-driven optimization.

Why It Matters

E-commerce platforms rely on post-ranking strategies to balance diversity and relevance but static configurations degrade over time, harming user experience. Automating refinement reduces manual effort, accelerates updates, and scales improvements across large recommendation systems, directly increasing key business metrics like orders and engagement.

Potential Customers & Pain Points

• E-commerce platforms – Manual post-ranking strategy updates are slow and costly
• Online marketplaces – Difficulty maintaining recommendation freshness
• Retailers with recommendation systems – Need to improve user engagement and sales efficiently

Business Model

SaaS platform or licensing model targeting large e-commerce companies, charging based on volume of recommendations processed or performance improvements delivered.

Competitive Landscape

• RecUserSim
• SimUSER
• Self-EvolveRec

Implementation Challenges

• Integration complexity with existing recommendation pipelines
• Ensuring robustness and safety of automated strategy updates
• Dependence on quality of user simulation and diagnosis accuracy

Validation Strategy

• Deploy pilot on mid-sized e-commerce platform to measure engagement lift
• Conduct A/B tests comparing manual vs automated refinement cycles
• Collect operational cost and cycle time data to quantify efficiency gains