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June 20, 2026Open Access

Exploring Recommender System Evaluation:A Multi-Modal LLM Agent Framework for A/B Testing

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Authors

WZWenlin ZhangXLX J LiQGQiyuan Ge

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Overview

Randomized trial evaluates A/B Agent's effectiveness in A/B testing, suggesting better model performance alternatives.

Key Points

  • This research aims to improve the evaluation of recommender systems through a novel multi-modal agent framework.
  • Developed a recommendation sandbox environment for realistic A/B testing.
  • Utilized a multi-modal agent that integrates user profiles, action memory retrieval, and fatigue simulation.
  • Validated the A/B Agent from multiple perspectives: model, data, and features.
  • Demonstrated that A/B Agent enhances recommendation model capabilities.
  • Found improvements in the alignment of user interactions to real online behaviors.
  • Validated the agent as a viable alternative to traditional online A/B testing.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a3632a0db0793dc1a539367https://doi.org/10.1145/3770854.3785688
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Also Consider

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  1. 1Large Language Model Agents for Recommender Systems: Bridging Behavior and Semantics with Long-Short Term Interest Modeling2026
  2. 2User Behavior Simulation with Large Language Model-based Agents2024 · 50 citations
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  4. 4Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents2024 · 7 citations
  5. 5An LLM-based Recommender System Environment2024