Machine Learning & Recommender Systems at Netflix Scale

Location:

Bayview A/B

Track:

Applied Machine Learning and Data Science

Time:

Tuesday, 10:30am - 11:20am

Abstract:

We at Netflix strive to deliver maximum enjoyment and entertainment to our millions of members across the world. One of the keys for innovating and improving our product is to leverage the vast amounts of user feedback we receive from our members every day. In order to turn data into knowledge, and knowledge into measurable business value, we need to take a multilayered approach that includes the use of smart system architectures and powerful machine learning algorithms as well as a data-driven approach to product innovation.

In this talk you will hear about some of the machine learning algorithms that power our recommender systems and the kind of data and features that drive them. You will also get an insight into the innovation approach that includes offline experimentation and online AB testing. Finally, you will learn about the system architectures that enable all of this at a Netflix scale.

Xavier Amatriain

Xavier Amatriain (PhD) is Director of Algorithms Engineering at Netflix. He leads a team of researchers and engineers designing the next wave of machine learning approaches to power the Netflix product. He is working on the cross-roads of machine learning research, large-scale software engineering, and product innovation. Previous to this, he was a Researcher in Recommender Systems, and neighboring areas such as Data Mining, Machine Learning, Information Retrieval, and Multimedia. He has authored more than 50 papers including book chapters, journals, and articles in international conferences. He has also lectured in different universities including the University of California Santa Barbara and UPF in Barcelona, Spain.