Statistical Significance of the Netflix Challenge

Inspired by the legacy of the Netflix contest, we provide an overview of what has been learned—from our own efforts, and those of others—concerning the problems of collaborative filtering and recommender systems. The data set consists of about 100 million movie ratings (from 1 to 5 stars) involving...

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Bibliographic Details
Published in:	Statistical science 2012-05, Vol.27 (2), p.202-231
Main Authors:	Feuerverger, Andrey, He, Yu, Khatri, Shashi
Format:	Article
Language:	English
Subjects:	Collaborative filtering Collaborative learning Computer science cross-validation Datasets Degrees of freedom effective number of degrees of freedom empirical Bayes ensemble methods gradient descent latent factors Machine learning Matrix Modeling Motion pictures Movies nearest neighbors Netflix contest neural networks Parametric models penalization prediction error Ratings & rankings recommender systems restricted Boltzmann machines shrinkage singular value decomposition Statistical analysis Text analytics User modeling World Wide Web
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