LinkedIn Endorsements: Bootstrapping a data product

Grand Ballroom - Salon A/B

How can large-scale machine learning be used to build products? At LinkedIn, the latest "data feature" in our portfolio is Endorsements, a mechanism to recognise someone for their skills and expertise. This ecosystem is generating a large graph of reputation signals: over 1B endorsements have been made in the few short months since the product launched.

How were we able to do this? In this talk, I'll deep dive into technical detail of our approach and the practical aspects of building a data feature like Endorsements. I'll go into how we extract a taxonomy of skills, how we determine if someone possesses a skill, and how we use that knowledge to recommend people to endorse. I'll also detail some of our open-source Hadoop-based infrastructure that allows us to put this into a productionized process.

Sam Shah's picture
Sam Shah is a principal engineer on the LinkedIn data team. He leads many of the site's large-scale recommendation and analytics systems, which analyze hundreds of terabytes of data daily to produce products and insights that serve LinkedIn's members. His work involves pure research, product-focused features, and infrastructure development, including social network analysis, recommendation engines, distributed systems, and grid computing. Some of the products under his purview include "People You May Know", "Whois Viewed My Profile?", Skills & Endorsements, and more. Sam holds a Ph.D. in Computer Science from the University of Michigan.