Today Netflix Corp. awarded its long-awaited $1M Grand Prize to team “BellKor’s Pragmatic Chaos,” which consisted of Bob Bell, Martin Chabbert, Michael Jahrer, Yehuda Koren, Martin Piotte, Andreas Töscher and Chris Volinsky.
The 3-year crowdsourcing contest motivated self-forming, unpaid volunteer teams to compete for one $1 million dollar prize by creating an algorithm which substantially improved by at least 10% the accuracy of Cinematch’s prediction about how much someone is going to enjoy a movie based on their movie preferences.
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.
And, from the Netflix Prize site:
It is our great honor to announce the $1M Grand Prize winner of the Netflix Prize contest as team BellKor’s Pragmatic Chaos for their verified submission on July 26, 2009 at 18:18:28 UTC, achieving the winning RMSE of 0.8567 on the test subset. This represents a 10.06% improvement over Cinematch’s score on the test subset at the start of the contest. We congratulate the team of Bob Bell, Martin Chabbert, Michael Jahrer, Yehuda Koren, Martin Piotte, Andreas Töscher and Chris Volinsky for their superb work advancing and integrating many significant techniques to achieve this result.
The Prize was awarded in a ceremony in New York City on September 21st, 2009. We will post a video on this forum of the presentation the team delivered about their Prize algorithm. In accord with the Rules the winning team has prepared a system description consisting of three papers, which we both make public below.
Team BellKor’s Pragmatic Chaos edged out team The Ensemble with the winning submission coming just 24 minutes before the conclusion of the nearly three-year-long contest. Historically the Leaderboard has only reported team scores on the quiz subset. The Prize is awarded based on teams’ test subset score. Now that the contest is closed we will be updating the Leaderboard to report team scores on both the test and quiz subsets.
To everyone who participated in the Netflix Prize: You’ve made this a truly remarkable contest and you’ve brought great innovation to the field. We applaud you for your contributions and we hope you’ve enjoyed the journey. The Netflix Prize contest is now closed.
We will soon be launching a new contest, Netflix Prize 2. Stay tuned for more details.
The winning team’s papers submitted to the judges can be found below. These papers build on, and require familiarity with, work published in the 2008 Progress Prize.
Y. Koren, “The BellKor Solution to the Netflix Grand Prize”, (2009).
A. Töscher, M. Jahrer, R. Bell, “The BigChaos Solution to the Netflix Grand Prize”, (2009).
M. Piotte, M. Chabbert, “The Pragmatic Theory solution to the Netflix Grand Prize”, (2009).
Congratulations BellKor’s Pragmatic Chaos.
For all of us – here’s to Netflix Prize 2…:
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.
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Tags: netflix, martin, prize contest, team, statisticians, unpaid volunteer, koren
— a competition that invited teams to try to improve on 