September 22, 2009, 12:00 AM

Amid Chaos, Netflix hands out $1 million for recommendations system

After almost three years of competition, Netflix today awarded a team of I.T. professionals $1 million for improving by 10% Netflix’s ability to predict which movies members will like. And Netflix announced another competition for further improvements.

After almost three years and submissions by more than 40,000 teams from 186 countries, Netflix Inc. today awarded the $1 million Netflix Prize to a team of engineers, statisticians and researchers who achieved the competition’s goal of a 10% improvement over the accuracy of the Netflix movie recommendation system when the competition was launched in October 2006. Netflix immediately announced a new $1 million contest to further improve its ratings system.

The team BellKor’s Pragmatic Chaos, the merging of three teams that had previously competed against one another, received the $1 million Netflix Prize in an award ceremony hosted by Netflix co-founder and CEO Reed Hastings and chief product officer Neil Hunt.

Netflix members already are benefiting from improvements Netflix Prize contestants have contributed to the recommendations system, the company says.

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, the company says, that it took a team of external and internal judges several weeks to validate the winner after the contest closed on July 26.

Chris Volinsky and Robert Bell, scientists who work on visualizing and analyzing large networks with AT&T; Labs-Research, were part of the team presented with the Netflix Prize.

“AT&T; Labs is focused on driving innovations that help customers do more and get more from communications and entertainment services,” says Dave Belanger, vice president of software and systems research and chief scientist, AT&T; Labs. “The efforts by Chris and Bob to advance the technologies behind recommendations systems could have far-reaching implications not only for Netflix but for AT&T;’s service portfolio, and potentially for other services and applications. Additionally, they display the collaborative nature of research and innovation today, working with other researchers across the globe online to build toward the goal.”

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. 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%, a hurdle Netflix scientists were not able to overcome on their own over the last decade.

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, the retailer explains.

As with the first Netflix Prize, the sequel will also be an open competition, and winning teams will retain ownership of their work, which they may license to Netflix and other companies. Success in this problem will enable businesses to deliver superior service to new customers much sooner than they can today, without requiring or waiting for the customer to provide the rich data points that underpinned the first Netflix Prize, says the e-retailer, No. 18 in the Internet Retailer Top 500 Guide.

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 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.

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