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. This kind of recommendation showed its value in the time when Netflix’s
business was primarily mailing DVDs. Nowadays Netflix has moved into the streaming world and this has spurred numerous changes
in the way people use the service. The service is now available on dozens of devices and more than 40 countries.
Instead of spending time deciding what to add to a DVD queue to watch later, people now access the service and watch whatever appeals to them at that moment. Also, Netflix now has richer contextual information such as the time and day when people are watching content, or the device they are using.
In this talk I will describe some of the ways we use implicit and contextualized information to create a personalized experience
for Netflix users.
Xavier Amatriain is currently managing a team of engineering/research stars in creating next generation personalized experiences at Netflix. He is working on the cross-roads of data mining, machine learning, software engineering, innovation, and agile methods.
Previous to this, he was a Senior Researcher in Telefonica, where his research focused on Recommender Systems and neighboring areas such as Data Mining, User Modeling, Social Networks, and e-Commerce.
He has authored more than 50 papers in books, journals and international conferences. He has also been invited speaker to several conferences and universities.
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