Back in 2006, movie rental powerhouse Netflix announced a competition: if someone—or a team of someones—could come up with a way to improve the service’s movie recommendation system by 10 percent, they’d wein $1 million. As expected, the competition drew interest from a wide variety of programmers and statisticians—and, naturally, Netflix began to reap the benefits of recommendation improvements investigated by the competitors without having to spend a dime of its own money on additional engineering and research efforts.
