Technology Review: What's Next for the Netflix Algorithms? When the Netflix Prize was awarded last month, it ended three years of intense competition aimed at finding a better algorithm for predicting users’ movie preferences.
The winning team, BellKor’s Pragmatic Chaos, was the first to forecast Netflix customers’ movie ratings with 10 percent better accuracy than the company’s in-house system–a feat that many experts believed would be impossible when the million-dollar prize was announced. Netflix plans to offer a second prize, this time for algorithms that predict movie preferences using more user information, such as gender, age, and zip code.
But experts say that the real challenge is to find ways to apply the lessons learned through the original Netflix challenge to other recommendation systems. At the end of October, experts in the field will meet at the ACM Conference on Recommender Systems in New York City to ask, among other things, what has been learned from the Netflix Prize. They Did It! One Team Reports Success in the $1m Netflix Prize. In October 2006 online movie rental company Netflix announced a contest called The Netflix Prize; any team that could beat its in-house recommendation engine by 10% in predicting which movies people would like would win a $1 million prize.
It was a huge engineering challenge that more than 50,000 teams of computer scientists signed up to take. Today one team, a combination of four of the front running teams actually, announced that it has built a system that delivers a 10.05% improvement. If that team withstands the month long period of scrutiny that begins now, it will not only mean fame and (some) fortune for them and a big boost for Netflix - it could signal a key turning point for recommendation technology on the web. The international team, called BellKor's Pragmatic Chaos, is made up of researchers from AT&T, Yahoo! Research Israel, Commendo Research and Consulting in Austria and Montreal's Pragmatic Theory. Bookmark suggestions from inSuggest.