At last, it's official: the research team known as BellKor has won the $1 million Netflix Prize by improving the accuracy of the Netflix recommender algorithm by 10 percent. It won because it submitted its entry 20 minutes before that of Ensemble, the only other team to make the 10 percent mark.
Netflix sure got its money's worth. CEO, Reed Hastings told the New York Times, “You look at the cumulative hours and you're getting Ph.D.'s for a dollar an hour.” Beyond the technical harvest, Netflix got the kind of halo effect that no company has enjoyed since 1987, when IBM's “Deep Blue” machine won a chess match against then-world champion Gary Kasparov.
Bellkor and Ensemble were both coalitions formed by teams that had to join forces to stay in the race. BellKor's own core, though, had always been the team to beat. Lead members Robert M. Bell, Chris Volinsky, and Yehuda Koren began their work at Bell Laboratories, where the first two still work; Koren has since joined Yahoo Research, in Israel. They snagged the competition's first, $50 000 milestone by achieving the best improvement, short of 10 percent, by a certain date. The three scientists, together with Jim Bennett, then a vice-president at Netflix, described their work for IEEE Spectrum, in “The Million-Dollar Programming Prize” (May 2009).
Of course, Netflix has launched a follow-up competition. This time, though, in the interest of brevity, it has set no particular target, deciding instead to award $500 000 to whoever's in the lead after six months and an equal sum to the leader after 18 months.
The competition has advanced the entire field of recommender systems, to the benefit of other companies. Earlier this year Tom Conrad, CTO of Pandora.com, the Internet radio company, told IEEE Spectrum that “we have benefited by peering inside the approaches tried by some of the thinking that went into the Netflix prize. We have incorporated some ideas into our own system.”