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The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users being identified except by numbers assigned for the contest.
The conference is the host of the ACM RecSys Challenge, a yearly competition in the spirit of the Netflix Prize focussing on a specific recommendation problem. The Challenge has been organized by companies such as Twitter, [14] and Spotify. [15]
From 2006 to 2009, Netflix sponsored a competition, offering a grand prize of $1,000,000 to the team that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company's existing recommender system.
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Netflix Chief Product Officer Eunice Kim discusses how the streamer recommends content and how the platform will evolve as other types of contents like games are added.
More money, more problems. That seems to be the motto of Netflix’s brand new competition series, in which one contestant is saddled with a $1 million prize as the other 11 players fight to hunt ...
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings. The prize would be awarded to the team achieving over 10% improvement over Netflix's own Cinematch algorithm. The team "Gravity" was the front runner during January—May 2007. [2]
Competition: October 1: Company: Netflix offers a $1,000,000 prize to the first developer of a video-recommendation algorithm that could beat its existing algorithm, Cinematch, at predicting customer ratings by more than 10%. [10] and uses the same 2016 icon 2007: January 15: Product: Netflix announces that it will launch streaming video. [11 ...