http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf

$1 million

The Netflix Prize sought to substantially improve the accuracy of predictions about how much someone is going to enjoy a movie based on their movie preferences.

The science of recommender systems is a prime beneficiary
of the contest. Many new people became involved in the
field and made their contributions. There is a clear spike
in related publications, and the Netflix dataset is the direct
catalyst to developing some of the better algorithms known in
the field. Out of the numerous new algorithmic contributions, I
would like to highlight one – those humble baseline predictors
(or biases), which capture main effects in the data. While
the literature mostly concentrates on the more sophisticated
algorithmic aspects, we have learned that an accurate treatment
of main effects is probably at least as significant as coming
up with modeling breakthroughs.
Finally, we were lucky to win this competition, but recognize
the important contributions of the many other contestants,
from which we have learned so much. We would like to thank
all those who published their results, those who participated
in the web forum, and those who emailed us with questions
and ideas. We got our best intuitions this way.

That is what the winning team of SCG competitions could say.