Netflix recently awarded their $1 million prize to a seven person group of statisticians and computer scientists for improving the accuracy of customer movie recommendations by ten percent.
Netflix, the movie rental giant, recently awarded their $1 million prize to a seven person group of statisticians and computer scientists for improving the accuracy of customer movie recommendations by ten percent. The contest lasted almost four years, and thousands of individuals threw their hats in the ring. Beyond just a simple coding issue, the contest challenged teams to consider cultural, demographic and even political categories when approaching the question of what each user would like to see next.
The large grand prize saved Netflix considerable time and effort to implement their own coding team. The contest was hailed in the media as a great use of crowd sourcing and Netflix immediately announced it will be looking into another contest with a new angle. The next contest unleashes a new massive data set of 100 million entries with demographic and behavioral information that the teams will have to manage to create “taste profiles.” The successful framework of a big prize with a vibrant and engaged community forum has allow Netflix to focus their business energy on other interests.
The New York Times Bits Blog discusses the implications of the contest:
The Netflix contest has been widely followed because its lessons could extend well beyond improving movie picks. The researchers from around the world were grappling with a huge data set — 100 million movie ratings — and the challenges of large-scale predictive modeling, which can be applied across the fields of science, commerce and politics.