3 citations · 3 across the 10 of their papers we have counts for
10 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…
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…
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…
LiGNN: Graph Neural Networks at LinkedIn
Fedor Borisyuk, Shihai He, Yunbo Ouyang +20
In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework. We share our insight on developing and deployment of GNNs at large scale at LinkedIn…