These methods aim to address a set of unique information retrieval challenges associated with talent search and recommendation systems, which can be listed as the following:

In this blog post, we discuss multiple methodologies we utilize in the talent search systems at LinkedIn. Save the profile to a hiring project (as a potential fit), and,.A screenshot from the application is presented in Figure 1.įigure 1: A (mocked) screenshot from the LinkedIn Recruiter productįor each recommended candidate, the recruiter can perform the following actions: Given a search request, candidates that match the request are selected and then ranked based on a variety of factors (such as the similarity of their work experience/skills with the search criteria, job posting location, and the likelihood of a response from an interested candidate) using machine-learned models in multiple passes. The LinkedIn Recruiter product provides a ranked list of candidates corresponding to a search request in the form of a query, a job posting, or a recommended candidate. We will then describe how we formulated and addressed these challenges, the overall system design and architecture, the issues encountered in practice, and the lessons learned from the production deployment of these systems at LinkedIn.

In this blog post, we will first highlight a few unique information retrieval, system, and modeling challenges associated with talent search and recommendation systems. A key mechanism to help achieve these goals is the LinkedIn Recruiter product, which helps recruiters and hiring managers source suitable talent and enables them to identify “ talent pools” that are optimized for the likelihood of making a successful hire.

LinkedIn Talent Solutions serves as a marketplace for employers to reach out to potential candidates and for job seekers to find career opportunities. Co-authors: Qi Guo, Sahin Cem Geyik, Cagri Ozcaglar, Ketan Thakkar, Nadeem Anjum, and Krishnaram Kenthapadi

Linkedin recruiter search