7 papers
Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search
Ping Liu, Qianqi Shen, Jianqiang Shen +11
Job-search platforms rely on low-bandwidth query interfaces that often fail to capture the high-dimensional complexity of candidate profiles. We present an end-to-end RLAIF (Reinfo…
A Unified Structured Query Understanding Framework for Industrial Semantic Search
Ping Liu, Qianqi Shen, Jianqiang Shen +15
Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components. While individually optimizable, this frag…
Policy-Grounded Dynamic Facet Suggestions for Job Search
Dan Xu, Baofen Zheng, Qianqi Shen +11
Job seekers often initiate search with short, underspecified queries. At LinkedIn, over 80% of job-related queries contain three or fewer keywords, making accurate user intent infe…
High Fidelity Textual User Representation over Heterogeneous Sources via Reinforcement Learning
Rajat Arora, Ye Tao, Jianqiang Shen +7
Effective personalization on large-scale job platforms requires modeling members based on heterogeneous textual sources, including profiles, professional data, and search activity…
Semantic Search At LinkedIn
Fedor Borisyuk, Sriram Vasudevan, Muchen Wu +71
Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present…
Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
Ping Liu, Jianqiang Shen, Qianqi Shen +9
Query understanding is essential in modern relevance systems, where user queries are often short, ambiguous, and highly context-dependent. Traditional approaches often rely on mult…