Following verification the winner also had to provide a non-exclusive license to Netflix. It consists of 4 text data files, each file contains over 20M rows, i.e. Netflix Prize data Dataset from Netflix's competition to improve their reccommendation algorithm. This is the dataset that was used in that competition. No information at all is provided about users. The more prominent ones were:On August 12, 2007, many contestants gathered at the KDD Cup and Workshop 2007, held at Over the second year of the competition, only three teams reached the leading position: This turned out to be an alternate name for Team BellKor.On November 13, 2007, team KorBell (formerly BellKor) was declared the winner of the $50,000 Progress Prize with an RMSE of 0.8712 (8.43% improvement).The 2008 Progress Prize was awarded to the team BellKor. By October 8, a team called WXYZConsulting had already beaten Cinematch's results.By October 15, there were three teams who had beaten Cinematch, one of them by 1.06%, enough to qualify for the annual progress prize.Over the first year of the competition, a handful of front-runners traded first place. over 4K movies and 400K customers. 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. Netflix held the Netflix Prize open competition for the best algorithm to predict user ratings for films. Their submission combined with a different team, BigChaos achieved an RMSE of 0.8616 with 207 predictor sets.This was the final Progress Prize because obtaining the required 1% improvement over the 2008 Progress Prize would be sufficient to qualify for the Grand Prize.
followed by Dinosaur Planet (RMSE = 0.8769; 7.83% improvement), and Gravity (RMSE = 0.8785; 7.66% improvement). In order to win the grand prize of $1,000,000, a participating team had to improve this by another 10%, to achieve 0.8572 on the test set.To win a progress or grand prize a participant had to provide source code and a description of the algorithm to the jury within one week after being contacted by them.
Once one of the teams succeeded to improve the RMSE by 10% or more, the jury would issue a The contest would last until the grand prize winner was declared. 695. Had no one received the grand prize, it would have lasted for at least five years (until October 2, 2011). The competition began on October 2, 2006. without the users or the films being identified except by numbers assigned for the contest.. (To keep their algorithm and source code secret, a team could choose not to claim a prize.)
Cinematch has a similar performance on the test set, 0.9525. In the last hour of the last call period, an entry by "KorBell" took first place. "The Ensemble" with a 10.10% improvement over Cinematch on the Qualifying set (a Quiz RMSE of 0.8553), and "BellKor's Pragmatic Chaos" with a 10.09% improvement over Cinematch on the Qualifying set (a Quiz RMSE of 0.8554).On September 18, 2009, Netflix announced team "BellKor's Pragmatic Chaos" as the prize winner (a Test RMSE of 0.8567), and the prize was awarded to the team in a ceremony on September 21, 2009.The joint-team "BellKor's Pragmatic Chaos" consisted of two Austrian researchers from Commendo Research & Consulting GmbH, Andreas Töscher and Michael Jahrer (originally team BigChaos), two researchers from The team reported to have achieved the "dubious honors" (On March 12, 2010, Netflix announced that it would not pursue a second Prize competition that it had announced the previous August. In 2007 two researchers from Dataset from Netflix's competition to improve their reccommendation algorithm 654. A team could send as many attempts to predict grades as they wish. Dataset. Learn more.
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