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The New York Times
A contest set up by Netflix [NFLX
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], which offered a $1 million prize to anyone who could significantly improve its movie recommendation system, ended on Sunday with two teams in a virtual dead heat, and no winner to be declared until September.
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But the contest, which began in October 2006, has already produced an impressive legacy. It has shaped careers, spawned at least one start-up company and inspired research papers. It has also changed conventional wisdom about the best way to build the automated systems that increasingly help people make online choices about movies, books, clothing, restaurants, news and other goods and services.
These so-called recommendation engines are computing models that predict what a person might enjoy based on statistical scoring of that person’s stated preferences, past consumption patterns and similar choices made by many others — all made possible by the ease of data collection and tracking on the Web.
“The Netflix prize contest will be looked at for years by people studying how to do predictive modeling,” said Chris Volinsky, a scientist at AT&T Research [T
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] and a leader of one of the two highest-ranked teams in the competition.
The biggest lesson learned, according to members of the two top teams, was the power of collaboration. It was not a single insight, algorithm or concept that allowed both teams to surpass the goal Netflix, the movie rental company, set nearly three years ago: to improve the movie recommendations made by its internal software by at least 10 percent, as measured by predicted versus actual one-through-five-star ratings by customers.
Instead, they say, the formula for success was to bring together people with complementary skills and combine different methods of problem-solving. This became increasingly apparent as the contest evolved. Mr. Volinsky’s team, BellKor’s Pragmatic Chaos, was the longtime front-runner and the first to surpass the 10 percent hurdle. It is actually a seven-person collection of other teams, and its members are statisticians, machine learning experts and computer engineers from the United States, Austria, Canada and Israel.
When BellKor’s announced last month that it had passed the 10 percent threshold, it set off a 30-day race, under contest rules, for other teams to try to best it. That led to another round of team-merging by BellKor’s leading rivals, who assembled a global consortium of about 30 members, appropriately called the Ensemble.
Submissions came fast and furious in the last few weeks from BellKor’s and the Ensemble. Just minutes before the contest deadline on Sunday, the Ensemble’s latest entry edged ahead of BellKor’s on the public Web leader board — by one-hundredth of a percentage point.
“The contest was almost a race to agglomerate as many teams as possible,” said David Weiss, a Ph.D. candidate in computer science at the University of Pennsylvania and a member of the Ensemble. “The surprise was that the collaborative approach works so well, that trying all the algorithms, coding them up and putting them together far exceeded our expectations.”
The contestants evolved, it seems, along with the contest. When the Netflix competition began, Mr. Weiss was one of three seniors at Princeton University, including David Lin and Lester Mackey, who made up a team called Dinosaur Planet. Mr. Lin, a math major, went on to become a derivatives trader on Wall Street.
But Mr. Mackey is a Ph.D. candidate at the Statistical Artificial Intelligence Lab at the University of California, Berkeley. “My interests now have been influenced by working on the Netflix prize contest,” he said.
Software recommendation systems, Mr. Mackey said, will increasingly become common tools to help people find useful information and products amid the explosion of information and offerings competing for their attention on the Web. “A lot of these techniques will propagate across the Internet,” he predicted.
That is certainly the hope of Domonkos Tikk, a Hungarian computer scientist and a member of the Ensemble. Mr. Tikk, 39, and three younger colleagues started working on the contest shortly after it began, and in 2007 they teamed up with the Princeton group. “When we entered the Netflix competition, we had no experience in collaborative filtering,” Mr. Tikk said.
Yet based on what they learned, Mr. Tikk and his colleagues founded a start-up, Gravity, which is developing recommendation systems for commercial clients, including e-commerce Web sites and a European cellphone company.
Though the Ensemble team nudged ahead of BellKor’s on the public leader board, it is not necessarily the winner. BellKor’s, according to Mr. Volinsky, remains in first place, and Netflix contacted it on Sunday to say so.
And in an online forum, another member of the BellKor’s team, Yehuda Koren, a researcher for Yahoo in Israel, said his team had “a better test score than the Ensemble,” despite what the rival team submitted for the leader board.
So is BellKor’s the winner? Certainly not yet, according to a Netflix spokesman, Steve Swasey. “There is no winner,” he said.
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A winner, Mr. Swasey said, will probably not be announced until sometime in September at an event hosted by Reed Hastings, Netflix’s chief executive. The movie rental company is not holding off for maximum public relations effect, Mr. Swasey said, but because the winner has not yet been determined.
The Web leader board, he explained, is based on what the teams submit. Next, Netflix’s in-house researchers and outside experts have to validate the teams’ submissions, poring over the submitted code, design documents and other materials. “This is really complex stuff,” Mr. Swasey said.
In Hungary, Mr. Tikk did not sound optimistic. “We didn’t get any notification from Netflix,” he said in a phone interview. “So I think the chances that we won are very slight. It was a nice try.”
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