Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems

• URL: http://arxiv.org/abs/2509.09690v1
• Date: Tue, 19 Aug 2025 21:35:43 GMT
• Title: Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
• Authors: Ping Liu, Jianqiang Shen, Qianqi Shen, Chunnan Yao, Kevin Kao, Dan Xu, Rajat Arora, Baofen Zheng, Caleb Johnson, Liangjie Hong, Jingwei Wu, Wenjing Zhang,
• Abstract summary: We introduce a unified query understanding framework powered by a Large Language Model (LLM)<n>Our approach jointly models the user query and contextual signals such as profile attributes to generate structured interpretations.<n>The framework improves relevance quality in online A/B testing while significantly reducing system complexity.
• Score: 10.9341814749217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models [57.94189874119267]
  Multi-Agent Systems (MAS) offer a powerful paradigm for solving complex problems.<n>Current graph learning-based design methodologies often adhere to a "one-for-one" paradigm.<n>We propose OFA-TAD, a one-for-all framework that generates adaptive collaboration graphs for any task described in natural language.
  arXiv  Detail & Related papers  (2026-01-19T12:23:44Z)
• Rules, Resources, and Restrictions: A Taxonomy of Task-Based Information Request Intents [0.6946929968559497]
  We argue for a stronger task-based perspective on query intent.<n>We present a taxonomy of task-based information request intents that bridges the gap between traditional query-focused approaches and the emerging demands of AI-driven task-oriented search.
  arXiv  Detail & Related papers  (2026-01-19T11:59:23Z)
• Scalable and Explainable Enterprise Knowledge Discovery Using Graph-Centric Hybrid Retrieval [0.0]
  Modern enterprises manage vast knowledge distributed across heterogeneous systems such as Jira, Git repositories, Confluence, and wikis.<n>We present a modular hybrid retrieval framework that integrates Knowledge Base Language-Augmented Models (KBLam), DeepGraph representations, and embedding-driven semantic search.<n>The framework builds a unified knowledge graph from parsed repositories including code, pull requests, and commit histories.<n> Experiments on large-scale Git repositories show that the unified reasoning layer improves answer relevance by up to 80 percent compared with standalone GPT-based retrieval pipelines.
  arXiv  Detail & Related papers  (2025-10-13T02:56:36Z)
• Reasoning-enhanced Query Understanding through Decomposition and Interpretation [130.19204432111277]
  ReDI is a Reasoning-enhanced approach for query understanding through Decomposition and Interpretation.<n>We compiled a large-scale dataset of real-world complex queries from a major search engine.<n>Experiments on BRIGHT and BEIR demonstrate that ReDI consistently surpasses strong baselines in both sparse and dense retrieval paradigms.
  arXiv  Detail & Related papers  (2025-09-08T10:58:42Z)
• Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations [0.0]
  We propose a hierarchical clustering-based retrieval method that eliminates the need to predefine k.<n>Our approach maintains the accuracy and relevance of system responses while adaptively selecting semantically relevant content.<n>Our framework is simple to implement and easily integrates with existing RAG pipelines, making it a practical solution for real-world applications under limited resources.
  arXiv  Detail & Related papers  (2025-06-16T15:34:29Z)
• CLEAR-KGQA: Clarification-Enhanced Ambiguity Resolution for Knowledge Graph Question Answering [13.624962763072899]
  KGQA systems typically assume user queries are unambiguous, which is an assumption that rarely holds in real-world applications.<n>We propose a novel framework that dynamically handles both entity ambiguity (e.g., distinguishing between entities with similar names) and intent ambiguity (e.g., clarifying different interpretations of user queries) through interactive clarification.
  arXiv  Detail & Related papers  (2025-04-13T17:34:35Z)
• QID: Efficient Query-Informed ViTs in Data-Scarce Regimes for OCR-free Visual Document Understanding [53.69841526266547]
  Fine-tuning a pre-trained Vision-Language Model with new datasets often falls short in optimizing the vision encoder.<n>We introduce QID, a novel, streamlined, architecture-preserving approach that integrates query embeddings into the vision encoder.
  arXiv  Detail & Related papers  (2025-04-03T18:47:16Z)
• Augmented Knowledge Graph Querying leveraging LLMs [2.5311562666866494]
  We introduce SparqLLM, a framework that enhances the querying of Knowledge Graphs (KGs)<n>SparqLLM executes the Extract, Transform, and Load (ETL) pipeline to construct KGs from raw data.<n>It also features a natural language interface powered by Large Language Models (LLMs) to enable automatic SPARQL query generation.
  arXiv  Detail & Related papers  (2025-02-03T12:18:39Z)
• Improving Retrieval in Sponsored Search by Leveraging Query Context Signals [6.152499434499752]
  We propose an approach to enhance query understanding by augmenting queries with rich contextual signals. We use web search titles and snippets to ground queries in real-world information and utilize GPT-4 to generate query rewrites and explanations. Our context-aware approach substantially outperforms context-free models.
  arXiv  Detail & Related papers  (2024-07-19T14:28:53Z)
• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• Meta Operator for Complex Query Answering on Knowledge Graphs [58.340159346749964]
  We argue that different logical operator types, rather than the different complex query types, are the key to improving generalizability. We propose a meta-learning algorithm to learn the meta-operators with limited data and adapt them to different instances of operators under various complex queries. Empirical results show that learning meta-operators is more effective than learning original CQA or meta-CQA models.
  arXiv  Detail & Related papers  (2024-03-15T08:54:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.