SFASA Jan. 23 Wed. Seminar -- Netflix Recommendations: Data, Models, and Statistics

 

Title:

 Netflix Recommendations: Data, Models, and Statistics

 

Time:  

Wed. Jan. 23rd. 4:30pm -6:30pm (coffee/snack from 4:30pm, presentation from 5:00pm)

 

Speaker: 

Dr. Xavier Amatriain, Director of Personalization Science and Engineering, Netfilx

 

Location:

San Jose State University,MacQuarrie Hall 520 (fifth floor)

 

 

You may find a map of the campus at http://www.sjsu.edu/map/  MacQuarrie Hall is identified as MQH and is in grid 2D on the map. 

 

Parking: For on-campus parking, one would probably park in the “south parking garage”, also in grid 2D on the map.  Public parking would be on the second floor and higher.  Pay stations are located at each end of each floor, where one can purchase a permit ($1/half hour, $8 max for the day).

 

Abstract:

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 to focusing on instant video streaming over the internet 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. These methods include a great variety of approaches: From the Matrix Factorization and Restricted Boltzmann Machines we learned during the Prize to advanced Learning to Rank approaches. 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 cycle that connects our machine learning experiments with the results of our AB tests, using data and statistics as our main decision mechanism.

 

Speaker Bio:

Xavier Amatriain is Director of Personalization Science and Engineering at Netfilx, where he leads a team working on defining the next generation of 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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