RAND Statistics Seminar Series
Lessons from the Netflix Prize
Presented by Dr. Robert Bell, AT&T Labs-Research, Florham Park, NJ
Thursday, October 1, 2009
10:30 a.m. – 12:00 p.m. PT / 1:30pm – 3:00pm ET
Conference Room 1226/1228
RAND Corporation, Santa Monica, CA
Please contact Denise Miller if you would like to attend this seminar.
In October 2006, the DVD rental company Netflix released more than 100 million user ratings of movies for a competition to predict users' ratings based on prior ratings. One allure to data analysts around the world was a $1,000,000 prize for a team achieving a ten percent reduction in root mean squared prediction error relative to Netflix's current algorithm. The size of the data (over 17,000 movies and 480,000 users) and the nature of human-movie interactions produced many modeling challenges. After describing some of the techniques in use and advances spurred by the competition, I will offer lessons and raise some questions about building massive prediction models, the role of statistics versus computer science in such endeavors, and prizes as a way to advance science. This is joint work with Chris Volinsky and Yehuda Koren, current and former colleagues at AT&T Labs-Research.
Robert Bell has been a member of the Statistics Research Department at AT&T Labs-Research since 1998. He previously spent about twenty years at RAND doing public policy analysis. His current research interests include machine learning methods, analysis of data from complex samples, and record linkage methods. In 2007 and 2008, he was a member of the teams that won the first two progress prizes in the Netflix Prize competition. He has served on the Fellows Committee of the American Statistical Association, the board of the National Institute of Statistical Sciences, the Committee on National Statistics, and several National Research Council advisory committees studying statistical issues from conduct of the decennial census to airline safety.
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