BellKor Wins Netflix $1 Million By 20 Minutes

eldavojohn writes "As we discussed at the time, there was a strange development at the end of Netflix's competition in which The Ensemble passed BellKor's Pragmatic Chaos by 0.01% a mere twenty minutes after BellKor had submitted results past the ten percent mark required to win the million dollars. Unfortunately for The Ensemble, BellKor was declared the victor this morning because of that twenty-minute margin. For those of you following the story, The New York Times reports on how teams merged to form Bellkor's Pragmatic Chaos and take the lead, which sparked an arms race of teams conjoining to merge their algorithms to produce better results. Now the Netflix Prize 2 competition has been announced." The Times blog quotes Greg McAlpin, a software consultant and a leader of the Ensemble: "Having these big collaborations may be great for innovation, but it's very, very difficult. Out of thousands, you have only two that succeeded. The big lesson for me was that most of those collaborations don't work."

Comments

Bad Summary

[Anonymous Coward]

The Ensemble beat BellKor by 0.01%... by their own reporting. According to Netflix, it was a tie. In the case of a tie, the first posted results wins.

Re:Bad Summary

[tangent3]

The Ensemble beat BellKor by 0.01% on the quiz set. Basically there are 2.8 million records in the qualifying set that the teams must predict the grades of. Half of the records (which half is known only to Netflix) form the quiz set, the other half form the test set. Teams submit their prediction a limit of once a day to get a result from the quiz set, but the final decision of who won is made on the result of the test set.

So even though Ensemble beat BellKor on the quiz set, the test set results came back dead even.

Re:The Objective

[crunchyeyeball]

Basically, you were asked to predict how a number of users would rate a number of movies, based on their previous ratings of other movies.

You were supplied with 100 million previous ratings (UserID, MovieID, Rating, DateOfRating), with the rating being a number beween 1 and 5 (5=best), and asked to make predictions for a seperate ("hidden") set comprising roughly 10% of the original data. You could then post a set of predictions to their website which would be automatically scored, and you'd receive a RMSE (Root Mean Squared Error) by email.

To avoid the possibility of tuning your predictions based on the RMSE, you could only post one submission per day, and the final competition-winning results would be scored against a seperate hidden set, independent of the daily scoring set.

It really was a fantastic competition, and anyone with a little coding knowledge (or SQL knowledge) could have a decent go at it. Personally, I scored an RMSE of 0.8969, or a 5.73% improvement over Netflix's benchmark Cinematch algorithm, having learnt a huge amount based on the published papers and forum postings of others in the contest, and my own incoherent theories.

In a way, everyone wins. Netflix gets a truly world-class prediction system based on the work of tens of thousands of researchers around the world hammering away for years at a time. Machine learning research moves a big step forward. BellKor et al get a big juicy cheque, and enthusiastic amateurs like myself get access to a huge amount of real-world research and data.