Today we’re joined by Tim Jurka, Head of Feed AI at LinkedIn.
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As you can imagine Feed AI is responsible for curating all the content you see daily on the LinkedIn site. What’s less apparent to those that don’t work on this type of product is the wide variety of opposing factors that need to be considered in organizing the feed. As you’ll learn in our conversation, Tim calls this the holistic optimization of the feed and we discuss some of the interesting technical and business challenges associated with trying to do this. We talk through some of the specific techniques used at LinkedIn like Multi-arm Bandits and Content Embeddings, and also jump into a really interesting discussion about organizing for machine learning at scale.
Thanks to our Sponsor!
I’d like to send a huge thanks to LinkedIn for sponsoring today’s show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of almost every product the company builds for its members and customers. LinkedIn’s highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.
Mentioned in the Interview
- Engineering at LinkedIn
- Problem Formulation for Machine Learning with Romer Rosales
- Productive Machine Learning at LinkedIn with Bee-Chung Chen
- Check out all of our great series from 2018 at the TWIML Presents: Series page!
- TWIML Online Meetup
- Register for the TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0