Research/Engineering Manager at Netflix
6:30 pm Networking, Snacks
7:00 pm Announcements
7:10 pm Presentation and Q&A
Netflix is known for pushing the envelope of recommendation technologies. In particular, the Netflix Prize put a focus on using explicit user feedback to predict ratings using data mining and machine learning techniques. Nowadays Netflix has moved into the streaming world and this has spurred numerous changes in the way people use the service. In this talk I will give an overview of the different techniques we currently use to personalize Netflix to the point that more than 75% of the things users select come from some sort of recommendation. I will describe how we deal with the different data sources and machine learning models and how we integrate them into a flexible architecture that can deal with large offline batch jobs as well as respond to real-time signals. Finally, I will talk about our offline-online innovation 0cycle that connects our machine learning experiments with the results of our AB tests.
Xavier Amatriain is currently managing a team of researchers and engineers creating next generation personalized experiences at Netflix. He is working on the cross-roads of machine learning, software engineering, innovation, and agile methods. Previous to this, he was a researcher focused on Recommender Systems and neighboring areas such as Data Mining, Machine Learning, User Modeling, and Social Networks. He has authored more than 50 papers in books, journals and international conferences.
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