Keynote: Next-Generation LinkedIn Talent Search

Abstract: Key challenge in talent search is to match the criteria of a hiring position with qualified candidates. Understanding of expertise that members might have and understanding of similar members who are qualified for same/similar position are fundamental and complementary data problems to bring personalized search experience into Next-Generation LinkedIn Recruiter, the de-facto largest Enterprise Recruiter Product and biggest monetization product at LinkedIn. During the talk, I will discuss how we pursue data driven approaches on expertise modeling and similar profiles modeling, and how we leverage such understanding to create the new ‘Search By Example’ paradigm for Talent Search at LinkedIn. 

Speaker biographyDr. Xianren (Ryan) Wu, is currently a staff machine learning scientist and tech lead at LinkedIn, where he primarily drives relevance development and innovation for LinkedIn Recruiter, the biggest (>1 billion revenue) monetization product in LinkedIn. He had successfully led the team to deliver core relevance improvements, and co-authored 10+ patents for the successful launch of Next-Generation LinkedIn Recruiter, the major product revamp during recent years. Before joining LinkedIn, he is a Co-Founder and Director of R&D at GageIn, Inc, responsible for the research and development of information extraction and business intelligence for the GageIn platform. He had driven the data product at Gagein from scratch to full-blown deployment with initial customer traction and persisting marketing momentum with steady revenue growth. He has authorized more than 20 articles, one book and won the Best Paper Award for SPECTS' 07 conference. Dr. Wu received his PhD in Electrical Engineering from University of California, Santa Cruz in 2008.


8:55-9:00 Welcome

9:00-9:40 Keynote: Next-Generation LinkedIn Talent Search

Ryan Wu, LinkedIn

9:40-10:10 I Know What You Coded Last Summer: Mining Candidate Expertise from GitHub Repositories

Rohit Saxena, Anuj Katiyal and Niranjan Pedanekar

10:10-10:20 NETWORK: A Capability And People Explorer System

Sarvnaz Karimi, Gaya K. Jayasinghe, David Ratcliffe and Alexander Krumpholz

10:20-10:30 A Visual Approach for Interactive Expertise Finding and Exploration

Ehsan Sherkat, Seyednaser Nourashrafeddin, Rosane Minghim and Evangelos Milios

10:30-11:00 Coffee break

11:00-11:25 ARISE-PIE: A People Information Integration Engine over the Web

Vincent W. Zheng, Tao Hoang, Penghe Chen, Yuan Fang, Xiaoyan Yang and Kevin Chen-Chuan Chang

11:25-11:50 An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse Datasets

 Janani Balaji, Faizan Javed, Mayank Kejriwal, Chris Min, Sam Sander and Ozgur Ozturk.

11:50-12:15 Towards Skills Taxonomy

Mohsen Sayyadiharikandeh and Raquel L. Hill

12:15-12:30 Discussions