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Search engines are crucial as they provide an efficient and easy way toaccess vast amounts of information on the internet for diverse informationneeds. User queries, even with a specific need, can differ significantly. Priorresearch has explored the resilience of ranking models against typical queryvariations like paraphrasing, misspellings, and order changes. Yet, these worksoverlook how diverse demographics uniquely formulate identical queries. Forinstance, older individuals tend to construct queries more naturally and invaried order compared to other groups. This demographic diversity necessitatesenhancing the adaptability of ranking models to diverse query formulations. Tothis end, in this paper, we propose a framework that integrates a novelrewriting pipeline that rewrites queries from various demographic perspectivesand a novel framework to enhance ranking robustness. To be specific, we useChain of Thought (CoT) technology to utilize Large Language Models (LLMs) asagents to emulate various demographic profiles, then use them for efficientquery rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE)architecture coupled with a hybrid loss function, collectively strengtheningthe ranking models' robustness. Our extensive experimentation on both publicand industrial datasets assesses the efficacy of our query rewriting approachand the enhanced accuracy and robustness of the ranking model. The findingshighlight the sophistication and effectiveness of our proposed model.

Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM