用大模型统一理解求职查询，提升匹配准确率与系统可扩展性。
Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
- 基于大模型联合分析用户查询与个人资料，生成结构化语义理解
- 在线测试显示推荐相关性显著提升，系统复杂度大幅降低
- 适合需要动态适应语言变化的高并发求职平台使用
在现代相关性系统中，用户查询通常简短、模糊且高度依赖上下文。传统方法常依赖多个特定任务的命名实体识别模型来提取结构化特征，如在求职应用中所示。然而，这种碎片化架构脆弱、维护成本高，且难以快速适应不断演变的分类体系和语言模式。本文提出一种由大语言模型（LLM）驱动的统一查询理解框架，旨在解决上述问题。该方法联合建模用户查询与上下文信号（如个人资料属性），生成结构化解释，从而驱动更准确、个性化的推荐。在线A/B测试结果表明，该方案提升了推荐相关性，同时显著降低了系统复杂性和运营开销。实验验证了该方法在动态网络应用中实现查询理解的可扩展性与适应性基础。
原文摘要 · Abstract (English)
Query understanding is essential in modern relevance systems, where user queries are often short, ambiguous, and highly context-dependent. Traditional approaches often rely on multiple task-specific Named Entity Recognition models to extract structured facets as seen in job search applications. However, this fragmented architecture is brittle, expensive to maintain, and slow to adapt to evolving taxonomies and language patterns. In this paper, we introduce a unified query understanding framework powered by a Large Language Model (LLM), designed to address these limitations. Our approach jointly models the user query and contextual signals such as profile attributes to generate structured interpretations that drive more accurate and personalized recommendations. The framework improves relevance quality in online A/B testing while significantly reducing system complexity and operational overhead. The results demonstrate that our solution provides a scalable and adaptable foundation for query understanding in dynamic web applications.
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