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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.
Second, the system executes a recommendation stage. It uses the most similar items to a user's already-rated items to generate a list of recommendations. Usually this calculation is a weighted sum or linear regression. This form of recommendation is analogous to "people who rate item X highly, like you, also tend to rate item Y highly, and you ...
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.
In practice, many commercial recommender systems are based on large datasets. As a result, the user-item matrix used for collaborative filtering could be extremely large and sparse, which brings about challenges in the performance of the recommendation. One typical problem caused by the data sparsity is the cold start problem. As collaborative ...
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Submissions for the Netflix Prize were judged using the RMSD from the test dataset's undisclosed "true" values. In the simulation of energy consumption of buildings, the RMSE and CV(RMSE) are used to calibrate models to measured building performance. [9]