Entity Personalized Talent Search Models with Tree Interaction Features

no code implementations • 25 Feb 2019 • Cagri Ozcaglar, Sahin Geyik, Brian Schmitz, Prakhar Sharma, Alex Shelkovnykov, Yiming Ma, Erik Buchanan

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting.

Benchmarking

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

no code implementations • 18 Sep 2018 • Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi

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.

Information Retrieval Recommendation Systems +1

Towards Deep and Representation Learning for Talent Search at LinkedIn

no code implementations • 17 Sep 2018 • Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, Sahin Cem Geyik

In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter.

Learning Semantic Representations Learning-To-Rank +1