Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915491222650880 |
|---|---|
| author | Liu, Ping Shen, Jianqiang Shen, Qianqi Yao, Chunnan Kao, Kevin Xu, Dan Arora, Rajat Zheng, Baofen Johnson, Caleb Hong, Liangjie Wu, Jingwei Zhang, Wenjing |
| author_facet | Liu, Ping Shen, Jianqiang Shen, Qianqi Yao, Chunnan Kao, Kevin Xu, Dan Arora, Rajat Zheng, Baofen Johnson, Caleb Hong, Liangjie Wu, Jingwei Zhang, Wenjing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09690 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems Liu, Ping Shen, Jianqiang Shen, Qianqi Yao, Chunnan Kao, Kevin Xu, Dan Arora, Rajat Zheng, Baofen Johnson, Caleb Hong, Liangjie Wu, Jingwei Zhang, Wenjing Information Retrieval Machine Learning 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. |
| title | Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2509.09690 |