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The Netflix Prize was an open competition for the best collaborative ... Netflix also identified a probe subset of 1,408,395 ratings within the training data set.
The Netflix Prize was a competition to predict how users would rate films based on their previous ratings of other films. The dataset contained data from 480,189 users who rated 17,770 movies. In 2008, with Arvind Narayanan, Shmatikov showed that it was possible to de-anonymize individual people from this dataset.
Netflix Prize: Movie ratings on Netflix. 100,480,507 ratings that 480,189 users gave to 17,770 movies Text, rating Rating prediction 2006 [5] Netflix: Amazon reviews US product reviews from Amazon.com. None. 233.1 million Text Classification, sentiment analysis 2015 (2018) [6] [7] McAuley et al. OpinRank Review Dataset
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]
The grand prize of $1,000,000 was reserved for the entry which bettered Netflix's own algorithm for predicting ratings by 10%. Netflix provided a training data set of over 100 million ratings that more than 480,000 users gave to nearly 18,000 movies, which is one of the largest real-life data sets available for research.
One of the events that energized research in recommender systems was the Netflix Prize. 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 ...
New algorithms have been developed for CF as a result of the Netflix prize. Cross-System Collaborative Filtering where user profiles across multiple recommender systems are combined in a multitask manner; this way, preference pattern sharing is achieved across models. [26]
The full data set contains 26,000,000 ratings and 750,000 tag applications applied to 45,000 movies by 270,000 users. It also includes tag genome data with 12 million relevance scores across 1,100 tags (Last updated 8/2017). [15] There are many types of research conducted based on the MovieLens data sets.