Netflix Prize competition. Like any Silicon Valley behemoth with an eye on the future, they would prefer to talk about what they're developing now. Mackey was mostly working as "The Blender," taking in all the algorithms and code produced by the various team members and trying to turn it into a final prediction. "We had, of course, not slept for the past two days because the contest was about to end," says Mackey. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Most importantly, the data was just plain interesting: an unruly mess of insights into taste, behavior, and pre-streaming viewer psychology.

LESSONS FROM THE NETFLIX PRIZE In 2006, Net ix announced the Net ix Prize, a machine learning and data mining competition for movie rating pre-diction.

"It was so much fun," says Mackey.

I'll look for that. This comes directly from the README: TRAINING DATASET FILE DESCRIPTION. Similarly, the organization Driven Data has crowd-sourced data competitions for socially-conscious causes. Objective Data manipulation Recommendation models. Here's mine. If I were to do it al again, I would use (Numba)[The original data is gone, I believe, but I might have it stored somewhere. Days prior we had actually managed to inch ahead of them. ", I competed alone in college. (Yes, most of these team names sound like titles for sci-fi novels. Mackey and his Ensemble teammates were feeling the heat.

Use Git or checkout with SVN using the web URL. Unbeknownst to him at the time, Zhou had lost his internet connection.Desperate, Mackey frantically searched through around a thousand files and found the right one, submitting it right as the last few seconds on the clock ticked away. Under the team name "Hi! Caltech CS 156b Project - Netflix Challenge.

3. Version 41 of 41.

It doesn't matter if you give critically acclaimed foreign dramas five stars if you spend all your waking hours bingeing A Netflix representative reached for comment says the company isn't particularly interested in reliving the past glories of the Netflix Prize. Or did BellKor have something else up their sleeve? Use Git or checkout with SVN using the web URL. The Netflix Prize was an open competition to find the best filtering algorithm for predicting how customers would rate specific movies, given rating data on thousands of movies and customers. Performed with 0.88212 test RMSE (7.2819% better than Netflix's original Cinematch).Note: If you run this code and get an error "terminate called after throwing an instance of 'char const*'", you have to place the data in the right directory (or modify readfile.cpp to change directory). As Gina Keating described in her book Hastings didn't have to dangle as much. We o ered $1 million to whoever improved the ac- curacy of our existing system called Cinematch by 10%. "We knew we were close to BellKor's Pragmatic Chaos but we didn't know if we had passed them for a day. A user who went by the pseudonym "Simon Funk" adapted an approach he'd previously worked on of incremental "singular value decomposition" (SVD), which, when applied to the Netflix Prize data, provided an automated method to finding similarities between the movies users loved or hated.

Unlike Volinsky or Lackey, he wasn't affiliated with a university or a research company. The range problem also applied to the individual titles: A blockbuster like Later in the competition, factors like what day users rated a movie on -- maybe you're a crankier critic on Mondays -- were incorporated into mathematical models built during late night coding sessions. Uninterested in working on the project anymore, "Funk" "We were all exchanging ideas," says DiCarlo, who continued to work on his own approach and follow the contest after "Funk" dropped out. "The contest was structured so well. "There was a fair amount of intrigue and backroom dealing going on," says Volinsky with a chuckle. By that point, Dinosaur Planet had been folded into a 30 person mega-team called The Ensemble, itself formed to compete with the super-group BellKor's Pragmatic Chaos. The recommender system became less about how you rate media and more about what you actually consume. )The BellKor squad included Chris Volinsky and his AT&T colleagues Robert Bell and Yehuda Koren, along with four other engineers from the United States, Austria, Canada and Israel. But would it be enough to win? More than 40 million people use GitHub to discover, fork, and contribute to over 100 million projects. "Everybody adopted Simon's ideas and then used them to come up with new ideas. Input (1) Execution Info Log Comments (22) This Notebook has been released under the Apache 2.0 … My Python Code for the Netflix Prize (github.com) 130 points by alexbw on Aug 24, 2012 | hide | past | web | favorite | 28 comments Nogwater on Aug 25, 2012 Copy and Edit.

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