Netflix Awards $1 Million Netflix Prize and Announces Second $1 Million Challenge

Netflix Awards $1 Million Netflix Prize and Announces Second $1 Million
Challenge

NEW YORK, Sept. 21 /PRNewswire/ -- After almost three years and submissions by
more than 40,000 teams from 186 countries, Netflix, Inc., the world's largest
online movie rental service (Nasdaq: NFLX), today awarded the $1 million
Netflix Prize to a team of engineers, statisticians and researchers who
achieved the competition's goal of a 10 percent improvement over the accuracy
of the Netflix movie recommendation system when the competition was launched
in Oct. 2006. Netflix members already are benefiting from improvements Netflix
Prize contestants have contributed to the recommendations system.

Moments after bestowing the $1 million prize, Netflix announced a second $1
million challenge, asking the world's computer science and machine learning
communities to keep the improvements coming.

The team "BellKor's Pragmatic Chaos," the merging of three teams that had
previously competed against one another in the contest, received the $1
million Netflix Prize in an award ceremony hosted here today by Netflix
Co-Founder and CEO Reed Hastings and Chief Product Officer Neil Hunt.

"We had a bona fide race right to the very end," said Mr. Hastings. "Teams
that had previously battled it out independently joined forces to surpass the
10 percent barrier. New submissions arrived fast and furious in the closing
hours and the competition had more twists and turns than 'The Crying Game,'
'The Usual Suspects' and all the 'Bourne' movies wrapped into one."
The winning team is comprised of software and electrical engineers,
statisticians and machine learning researchers from Austria, Canada, Israel
and the United States. All seven team members - Bob Bell, Martin Chabbert,
Michael Jahrer, Yehuda Koren, Martin Piotte, Andreas Toscher and Chris
Volinsky - attended the awards ceremony. It was the first time all seven had
met one another in person. How the $1 million is split is to be determined by
the team.
Netflix said "BellKor's Pragmatic Chaos" edged out a team called "The
Ensemble," another collaboration of former competitors, with the winning
submission coming just 24 minutes before the conclusion of the nearly
three-year-long contest. The competition was so close and the submissions so
sophisticated that it took a team of external and internal judges several
weeks to validate the winner after the contest closed on July 26. The Netflix
Prize external judges are University of California, San Diego Professor
Charles Elkan and University of California, Irvine Professor Padhraic Smyth.
The internal judges are Netflix senior engineers Stanley Lanning and Jon
Sanders.

The contest's rules require the winning team to publish its methods so that
businesses in many fields can benefit from the work done. The winning
submission and the previously hidden ratings used to score the contest will be
published at the University of California Irvine Machine Learning Repository.
The team licensed its work to Netflix and is free to license it to other
companies.

When Netflix launched the Netflix Prize in October 2006, it made available to
contestants 100 million anonymous movie ratings ranging from one to five
stars, the largest such data set ever released. All personal information
identifying individual Netflix members was removed from the prize data, which
contained only movie titles, star ratings and dates but no text reviews. The
challenge was to improve upon the company's ability to accurately predict
Netflix members' movie tastes by 10 percent, a hurdle Netflix scientists were
not able to overcome on their own over the last decade.

"Accurately predicting the movies Netflix members will love is a key component
of our service," said Dr. Hunt. "This extreme level of personalization is
like entering a video store with 100,000 titles and having those that are most
interesting to you fly off the shelves and line up in front of you. We take
the guess work out of renting by presenting the movies and TV episodes we
believe each Netflix member will most enjoy," he added.

Netflix Prize 2 - The Next $1 Million Challenge

While the first Netflix Prize solved the tough challenge of accurately
predicting movie enjoyment by Netflix members who have provided ratings on an
average of 50 or more other movies, Netflix Prize 2 focuses on the much harder
problem of predicting movie enjoyment by members who don't rate movies often,
or at all, by taking advantage of demographic and behavioral data carrying
implicit signals about the individuals' taste profiles. As with the first
Netflix Prize, the sequel will also be an open competition with winning teams
owning their solution to license to Netflix and other companies. Success in
this problem will enable businesses to deliver superior service to new
customers much sooner in their lifecycle, without requiring or waiting for the
customer to provide the rich data points that underpinned the first Netflix
Prize.

The new data set, providing more than 100 million data points, will include,
among other things, information about renters' ages, genders, ZIP codes, genre
ratings and previously chosen movies. As with the first Netflix Prize, all
data provided is anonymous and cannot be associated with a specific Netflix
member.

Unlike the first challenge, this contest has no specific accuracy target. In
fact, Netflix said today that the company and the judges have little idea how
far the world's foremost experts can push this data to derive useful
predictions. Instead, $500,000 will be awarded to the team judged to be
leading after six months and an additional $500,000 will be given to the team
in the lead at the 18-month mark, when the contest is wrapped up. Once again,
Netflix will require the winning team to publish its methods.

The Netflix recommendation engine spans the 100,000 DVD titles in the Netflix
catalog and is an essential element of the company's movie subscription
service. Each of the 10.6 million Netflix members enjoys a personalized
member Web site that enables them to rate movies on a one to five star scale.
Netflix combines those individual ratings into a database of more than three
billion movie ratings and employs proprietary algorithms and software to
identify movies that tend to be rated highly (or poorly) by people with
similar tastes. Netflix has already enhanced these algorithms using
innovations from the winners of two annual Netflix Progress Prize awards. The
accuracy of this software has been praised by movie critics and members alike
and enables Netflix to fulfill its goal of connecting people with movies
they'll love.

Complete details about the Netflix Prize are available at
www.netflixprize.com.

About Netflix, Inc.
Netflix, Inc. (NASDAQ: NFLX) is the world's largest online movie rental
service, with more than 10 million subscribers. For only $8.99 a month,
Netflix members can instantly watch unlimited movies and TV episodes streamed
to their TVs and computers and can receive unlimited DVDs delivered quickly to
their homes. There are never any due dates or late fees. Netflix members can
exchange DVDs as often as they want using a postage-paid return envelope.
Members can choose from a vast selection of DVD titles and a growing library
of movies and TV episodes that can be watched instantly. Netflix is
partnering with leaders in consumer electronics to bring to market a range of
devices that can instantly stream movies and TV episodes from Netflix directly
to members' TVs. These devices currently include Blu-ray disc players and new
Internet TVs from LG Electronics; Blu-ray disc players from Samsung; the Roku
digital video player; Microsoft's Xbox 360 game console; TiVo digital video
recorders; and, soon, Internet TVs from Sony and VIZIO. For more information,
visit http://www.netflix.com/.

SOURCE Netflix, Inc.

Steve Swasey, Netflix, Inc., +1-408-540-3947, sswasey@netflix.com