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Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form <user, movie, date of grade, grade> . The user and movie fields are integer IDs, while grades are from 1 to 5 ( integer ) stars.
One example is the movie-ratings matrix, as appears in the Netflix problem: Given a ratings matrix in which each entry (,) represents the rating of movie by customer , if customer has watched movie and is otherwise missing, we would like to predict the remaining entries in order to make good recommendations to customers on what to watch next.
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.
The past 24 hours have been awash in headlines about Netflix’s What We Watched report: an information dump that many analysts considered the most transparent accounting of user data that the ...
Netflix is expanding its viewership data transparency with the release of its first biannual report covering six months of streaming habits on the platform, revealing for the first time how ...
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]
Ted Sarandos, Netflix’s co-CEO and chief content officer, revealed what he said was the “most comprehensive look so far” at the streamer’s top 10 TV shows and movies. Sarandos, in an ...
In 2010, Netflix canceled a running contest to improve the company's recommendation algorithm due to privacy concerns: under the terms of the competition, contestants were given access to customer rental data, which the company had purportedly anonymized.