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 a reward-driven rollback mechanism, enabling continuous and safe 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 and revenue. Automating refinement accelerates adaptation to evolving user behavior, improving key metrics and reducing manual operational costs. This scalable approach enhances recommendation quality continuously without extensive human intervention.

Potential Customers & Pain Points

• E-commerce platforms – Manual post-ranking strategy updates are slow and costly
• Online retailers – Difficulty maintaining recommendation freshness and diversity
• Recommendation system operators – Need to reduce operational overhead and improve user engagement metrics

Business Model

SaaS platform or API licensing to e-commerce companies, with tiered pricing based on transaction volume and feature access; potential for consulting and customization services.

Competitive Landscape

• RecUserSim
• SimUSER
• Self-EvolveRec

Implementation Challenges

• Integration complexity with existing recommendation pipelines
• Ensuring safe and reversible strategy updates in live environments
• Dependence on accurate user simulation for diagnosis

Validation Strategy

• Conduct extended A/B testing on multiple e-commerce platforms to measure impact on key metrics
• Benchmark against existing post-ranking refinement methods and LLM-based agents
• Gather customer feedback on operational cost savings and ease of integration