Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
Not on the map. Why?
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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Sep 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 2; 2024: 1; 2025: 2; 2026: 4.
11 papers.
- 2023A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analyticscited by 18
- 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
- 2023Disentangling and Operationalizing AI Fairness at LinkedIncited by 7
- 2024Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrievalcited by 2
- 2025Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendationscited by 2
- 2025Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptionscited by 2
- 2022Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systemscited by 0
- 2026Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentationcited by 0
- 2026Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Searchcited by 0
- 2026Personalized and Aspiration-Oriented Career Path Recommendationcited by 0
- 2026From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Searchcited by 0
- 2026From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search29 Aug 2026
- 2026Personalized and Aspiration-Oriented Career Path Recommendation22 Aug 2026
- 2026Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search25 Jun 2026
- 2026Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation23 Apr 2026
- 2025Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions5 Oct 2025
- 2025Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations8 Jan 2025
- 2024Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval18 Sep 2024
- 2023A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics3 Jul 2023
- 2023Disentangling and Operationalizing AI Fairness at LinkedIn30 May 2023
- 2022Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systems8 Sep 2022
- 2022Recommendation Systems with Distribution-Free Reliability Guarantees4 Jul 2022