MAAQR: An LLM-based Multi-Agent Framework for Adaptive Query Rewriting in Alipay Search
Qi Zheng, Mingjie Zhong, Saisai Gong, Huimin Jiang, Kaixin Wu, Hong Liu, Jia Xu, Linjian Mo · 2025
Query rewriting is essential in e-commerce search, as it bridges the lexical gap between user queries and item descriptions, thereby enhancing search performance.Despite recent advancements, current rewriting approaches are still limited by an inadequate comprehension of domain-specific knowledge and a lack of mechanisms for adaptive refinement in response to new or changing queryitem relationships.To overcome these limitations, we propose a large language model (LLM) based Multi-Agent Framework for Adaptive Query Rewriting (MAAQR) in Alipay Search.Initially, we perform knowledge-enhanced fine-tuning to improve the LLM's understanding of query and item semantics.Subsequently, a multiagent collaborative rewriting architecture is employed to enhance rewrite quality and adaptability.MAAQR has been successfully deployed to serve Alipay's mini-app search since December 2024.Through offline experiments and online A/B testing, MAAQR significantly improves click-through rates (CTR) and the number of transactions for target queries, while substantially reducing the zero-results rate (ZRR).