Abstract
The essence of modern e-Commercial search system lies in matching user's intent and available candidates depending on user's query, providing personalized and precise service. However, user's query may be incorrect due to ambiguous input and typo, leading to inaccurate search. These cases may be released by query rewrite: modify query to other representation or expansion. However, traditional query rewrite replies on static rewrite vocabulary, which is manually established meanwhile lacks interaction with both domain knowledge in e-Commercial system and common knowledge in the real world. In this paper, with the ability to generate text content of Large Language Models (LLMs), we provide an iterative framework to generate query rewrite. The framework incorporates a 3-stage procedure in each iteration: Rewrite Generation with domain knowledge by Retrieval-Augmented Generation (RAG) and query understanding by Chain-of-Thoughts (CoT); Online Signal Collection with automatic positive rewrite update; Post-training of LLM with multi task objective to generate new rewrites. Our work (named as IterQR) provides a comprehensive framework to generate \textbf{Q}uery \textbf{R}ewrite with both domain / real-world knowledge. It automatically update and self-correct the rewrites during \textbf{iter}ations. \method{} has been deployed in Meituan Delivery's search system (China's leading food delivery platform), providing service for users with significant improvement.
Abstract (translated)
现代电子商务搜索系统的核心在于根据用户的查询匹配用户意图与可用候选，从而提供个性化和精准的服务。然而，由于输入模糊或拼写错误等原因，用户的查询可能不准确，导致搜索结果偏差。这种情况可以通过查询重写来缓解：将原始查询修改为其他形式或扩展其含义。但是，传统的查询重写依赖于静态的重写词汇表，这些词汇表是手动创建且缺乏与电子商务系统中的领域知识和现实世界常识的互动。 在本文中，借助大型语言模型（LLM）生成文本内容的能力，我们提出了一种迭代框架来生成查询重写。该框架每轮包含三个步骤：使用检索增强生成（RAG）和链式思维（CoT）进行查询理解与领域知识相结合的重写生成；在线信号收集以自动更新正面重写；以及通过多任务目标对LLM进行后训练，以便产生新的重写。 我们的工作（命名为IterQR）提供了一个综合框架来生成带有领域/现实世界知识的**Q**uery **R**ewrite。在迭代过程中，它能够自动更新和自我纠正重写内容。该方法已被部署在中国领先的外卖平台美团配送的搜索系统中，并为用户提供了显著的服务改进。
URL
https://arxiv.org/abs/2504.05309
