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

From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach

no code implementations • 3 Sep 2017 • Viet Ha-Thuc, Yan Yan, Xianren Wu, Vijay Dialani, Abhishek Gupta, Shakti Sinha

One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position.

Position

Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn

no code implementations • 26 Feb 2016 • Viet Ha-Thuc, Ye Xu, Satya Pradeep Kanduri, Xianren Wu, Vijay Dialani, Yan Yan, Abhishek Gupta, Shakti Sinha

This new system only needs the searcher to input one or several examples of suitable candidates for the position.

Position