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Ruiming Tang

2 papers indexed

openalexCityU Scholars2026-08-01

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

Wenlin Zhang, X J Li, Qiyuan Ge, Kuicai Dong, Pengyue; id_orcid 0000-0003-4712-3676 Jia, X J Li, et al.

diningIn recommender systems, online A/B testing is a crucial method for evaluating the performance of different models. However, conducting online A/B testing often presents significant challenges, including substantial economic costs, user experience degradation, and considerab…

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arxivcs.AIcs.CLcs.IR2026-06-25

AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems

Changxin Lao, Fei Pan, Guozhuang Ma, Han Li, Huihuang Lin, Jijun Shi, et al.

Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remains blocked by a structural execution bottleneck: the idea-to-launch cycle still depends on human engineers to generate hypothese…

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