mlsanityData to 14 Oct 2026

SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation

arXiv 2607.17719 ↗First posted 20 Jul 2026cs.AI cs.MAcited by 0

On the map: LLM agents and tool use → Agentic recommender systems

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 24 of its 36 references that are in the map. First posted on arXiv 20 Jul 2026.

Builds on

24 of 36 references.

Closest ancestors · 10

Same direction, newest first

  1. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems5 Jul
  2. 2026Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents10 Feb
  3. 2025MADREC: A Multi-Aspect Driven LLM Agent for Explainable and Adaptive Recommendation15 Oct
  4. 2025AgentRec: Next-Generation LLM-Powered Multi-Agent Collaborative Recommendation with Adaptive Intelligence2 Oct
  5. 2025Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems21 Sep
  6. 2025A Survey on LLM-powered Agents for Recommender Systems14 Feb
  7. 2023On Generative Agents in Recommendation16 Oct
  8. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems13 Oct
  9. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations31 Aug
  10. 2023RecMind: Large Language Model Powered Agent For Recommendation28 Aug

Other references · 14

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  3. 2024Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judgecited by 269
  4. 2018Top-K Off-Policy Correction for a REINFORCE Recommender Systemcited by 182
  5. 2019A Survey on Session-based Recommender Systemscited by 97
  6. 2024Evaluating Large Language Models as Generative User Simulators for Conversational Recommendationcited by 48
  7. 2024LLM-Powered User Simulator for Recommender Systemcited by 39
  8. 2025SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluationcited by 33
  9. 2025RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systemscited by 12
  10. 2026Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedbackcited by 10
  11. 2026AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systemscited by 8
  12. 2022Determinantal Point Process Likelihoods for Sequential Recommendationcited by 7
2 more
  1. 2023Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networkscited by 7
  2. 2025StepFly: Agentic Troubleshooting Guide Automation for Incident Diagnosiscited by 5

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
  2. 2026A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing
  3. 2026QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation
  4. 2026EvoRec: Self Evolving Agentic Recommender Systems
  5. 2025Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
  6. 2024Agentic Feedback Loop Modeling Improves Recommendation and User Simulation
  7. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
  8. 2026Advancing Model Research in AgentX: Long-Horizon Autonomy for Industrial Recommender Systems