Wednesday, October 11, 2006

like barton fink? try anchorman!

The recommendations from Amazon, if tended carefully with preferences updated regularly, are so good that I sense I could reach a point where all of the approximately 500 items they suggest I want, I would actually want. There needs to be a term for the extreme conclusion of this elusive state of affinity between you and your "collaborative filtering system." Netflix, on the other hand, has rarely if ever suggested something I would like. Admitting you have a problem is the first step, so I was glad to see Netflix is turning to its customers for help, and there's a cool $1 million up for grabs.

To win the prize...a contestant will have to devise a system that is more accurate than the company’s current recommendation system by at least 10 percent. And to improve the quality of research, Netflix is making available to the public 100 million of its customers’ movie ratings, a database the company says is the largest of its kind ever released.

Recommendation systems, also known as collaborative filtering systems, try to predict whether a customer will like a movie, book or piece of music by comparing his or her past preferences to those of other people with similar tastes. Such systems will look at, say, the last 10 books, movies or songs a customer has rated highly and try to extrapolate an 11th.

Computer scientists say that after years of steady progress in this field, there has been a slowdown — which is what Netflix executives say prompted them to offer the problem to a wide audience for solution.