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Holistic Optimization of the LinkedIn News Feed - TWiML Talk #224 episode artwork

EPISODE · Jan 28, 2019 · 48 MIN

Holistic Optimization of the LinkedIn News Feed - TWiML Talk #224

from The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) · host Sam Charrington

Today we’re joined by Tim Jurka, Head of Feed AI at LinkedIn. In our conversation, Tim describes 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.

What this episode covers

Today we’re joined by Tim Jurka, Head of Feed AI at LinkedIn. In our conversation, Tim describes 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.

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Holistic Optimization of the LinkedIn News Feed - TWiML Talk #224

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Frequently Asked Questions

How long is this episode of The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)?

This episode is 48 minutes long.

When was this The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) episode published?

This episode was published on January 28, 2019.

What is this episode about?

Today we’re joined by Tim Jurka, Head of Feed AI at LinkedIn. In our conversation, Tim describes the holistic optimization of the feed and we discuss some of the interesting technical and business challenges associated with trying to do this. We...

Can I download this The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) episode?

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