SR-Agent: First LLM Agent Framework for Automated Post-Ranking Strategy Refinement in E-Commerce
Researchers present SR-Agent, an agentic framework designed to automate the refinement of post-ranking strategies in industrial e-commerce recommender systems. The system integrates three specialized LLM agents to identify user-perceived issues, diagnose recurring problems, and implement constrained strategy updates. In a one-month A/B test on the Kuaishou platform, SR-Agent improved order volume by 0.71%, browsing depth by 0.34%, and category diversity by 0.48%, while reducing operational costs.
Why it matters: This work demonstrates a practical, deployed LLM agent system that autonomously enhances e-commerce recommendation quality, delivering measurable business impact and reducing manual intervention.
Full story at: arXiv Multiagent Systems ↗