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

TL;DR

This paper discusses the practical challenges and lessons learned from implementing talent search and recommendation systems at LinkedIn, focusing on real-world IR, system, and modeling issues.

Contribution

It provides insights into the unique challenges faced in building large-scale talent search and recommendation systems and shares practical lessons learned from LinkedIn's experience.

Findings

Identification of key IR challenges in talent search

System design considerations for large-scale recommendation systems

Lessons learned from deploying LinkedIn Recruiter features

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.