# Deep Job Understanding at LinkedIn

arXiv: https://arxiv.org/abs/2006.12425 · HTML: https://mlsanity.com/paper/2006.12425/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 12 of 12)

- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings](https://mlsanity.com/paper/2402.05617/) (2024)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning](https://mlsanity.com/paper/2202.10739/) (2022)
- [Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings](https://mlsanity.com/paper/2401.05073/) (2024)
- [Learning to Retrieve for Job Matching](https://mlsanity.com/paper/2402.13435/) (2024)
- [CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship](https://mlsanity.com/paper/2408.15620/) (2024)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Job recommendations: benchmarking of collaborative filtering methods for classifieds](https://mlsanity.com/paper/2301.07946/) (2023)
- [Multi-generational labour markets: data-driven discovery of multi-perspective system parameters using machine learning](https://mlsanity.com/paper/2302.10146/) (2023)
- [Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting](https://mlsanity.com/paper/2409.19545/) (2024)
- [Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn](https://mlsanity.com/paper/2607.24783/) (2026)

## Abstract (arXiv)

As the world's largest professional network, LinkedIn wants to create economic opportunity for everyone in the global workforce. One of its most critical missions is matching jobs with processionals. Improving job targeting accuracy and hire efficiency align with LinkedIn's Member First Motto. To achieve those goals, we need to understand unstructured job postings with noisy information. We applied deep transfer learning to create domain-specific job understanding models. After this, jobs are represented by professional entities, including titles, skills, companies, and assessment questions. To continuously improve LinkedIn's job understanding ability, we designed an expert feedback loop where we integrated job understanding models into LinkedIn's products to collect job posters' feedback. In this demonstration, we present LinkedIn's job posting flow and demonstrate how the integrated deep job understanding work improves job posters' satisfaction and provides significant metric lifts in LinkedIn's job recommendation system.