# Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned

arXiv: https://arxiv.org/abs/1809.06481 · HTML: https://mlsanity.com/paper/1809.06481/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Recommendation Systems with Distribution-Free Reliability Guarantees](https://mlsanity.com/paper/2207.01609/) (2022)
- [Disentangling and Operationalizing AI Fairness at LinkedIn](https://mlsanity.com/paper/2306.00025/) (2023)
- [Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval](https://mlsanity.com/paper/2409.12097/) (2024)
- [Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations](https://mlsanity.com/paper/2501.04762/) (2025)
- [Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions](https://mlsanity.com/paper/2512.00004/) (2025)
- [Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systems](https://mlsanity.com/paper/2209.03819/) (2022)
- [Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation](https://mlsanity.com/paper/2604.21264/) (2026)
- [Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search](https://mlsanity.com/paper/2606.27291/) (2026)
- [Personalized and Aspiration-Oriented Career Path Recommendation](https://mlsanity.com/paper/2608.22056/) (2026)
- [From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search](https://mlsanity.com/paper/2608.28965/) (2026)

## Abstract (arXiv)

LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities. LinkedIn's job ecosystem has been designed as a platform to connect job providers and job seekers, and to serve as a marketplace for efficient matching between potential candidates and job openings. A key mechanism to help achieve these goals is the LinkedIn Recruiter product, which enables recruiters to search for relevant candidates and obtain candidate recommendations for their job postings. In this work, we highlight a set of unique information retrieval, system, and modeling challenges associated with talent search and recommendation systems.