SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategies Refinement in E-Commerce Recommendation
SR-Agent is a new agentic framework designed to automate the refinement of post-ranking strategies in industrial e-commerce recommender systems. It integrates three specialized agents to identify user-perceived issues, diagnose recurring problems, and apply constrained refinements with rollback capability. In a one-month A/B test on the Kuaishou platform, SR-Agent increased order volume by 0.71%, browsing depth by 0.34%, and clicked-category diversity by 0.48%, while reducing manual effort and operational costs.
Why it matters: SR-Agent represents the first deployed framework to fully automate the loop of refining post-ranking strategies in industrial recommender systems, demonstrating measurable improvements in key e-commerce metrics and operational efficiency.
Full story at: arXiv Multiagent Systems ↗