  Search engines are crucial as they provide an efficient and easy way to
access vast amounts of information on the internet for diverse information
needs. User queries, even with a specific need, can differ significantly. Prior
research has explored the resilience of ranking models against typical query
variations like paraphrasing, misspellings, and order changes. Yet, these works
overlook how diverse demographics uniquely formulate identical queries. For
instance, older individuals tend to construct queries more naturally and in
varied order compared to other groups. This demographic diversity necessitates
enhancing the adaptability of ranking models to diverse query formulations. To
this end, in this paper, we propose a framework that integrates a novel
rewriting pipeline that rewrites queries from various demographic perspectives
and a novel framework to enhance ranking robustness. To be specific, we use
Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as
agents to emulate various demographic profiles, then use them for efficient
query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE)
architecture coupled with a hybrid loss function, collectively strengthening
the ranking models' robustness. Our extensive experimentation on both public
and industrial datasets assesses the efficacy of our query rewriting approach
and the enhanced accuracy and robustness of the ranking model. The findings
highlight the sophistication and effectiveness of our proposed model.
