Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
September 18, 2018 Β· Declared Dead Β· π Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
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Authors
Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi
arXiv ID
1809.06481
Category
cs.AI: Artificial Intelligence
Citations
48
Venue
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Last Checked
3 months ago
Abstract
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.
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