3 citations · 3 across the 9 of their papers we have counts for
9 papers
Embedding Subspace Partitioning for Dynamic Multi-Objective Retrieval
Shaobo Zhang, Alice Leung, Yunxiang Ren +13
Modern industrial recommender systems must optimize across competing objectives, balancing semantic relevance with business metrics such as engagement and revenue. While bi-encoder…
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…
Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn
Dan Xu, Baofen Zheng, Jianqiang Shen +11
Job understanding is critical to LinkedIn's mission of connecting talent with opportunity. This task involves transforming unstructured and noisy job postings into standardized or…
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…
LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation
Zhoutong Fu, Yihan Cao, Yi-Lin Chen +16
Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applica…
A Scalable and Efficient Signal Integration System for Job Matching
Ping Liu, Rajat Arora, Xiao Shi +13
LinkedIn, one of the world's largest platforms for professional networking and job seeking, encounters various modeling challenges in building recommendation systems for its job ma…
Learning to Retrieve for Job Matching
Jianqiang Shen, Yuchin Juan, Shaobo Zhang +21
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often in…
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks
Ping Liu, Haichao Wei, Xiaochen Hou +11
We present LinkSAGE, an innovative framework that integrates Graph Neural Networks (GNNs) into large-scale personalized job matching systems, designed to address the complex dynami…