The Netflix Prize, Big Data, SVD and R

One of the key data analysis tools that the BellKor team used to win the Netflix Prize was the Singular Value Decomposition (SVD) algorithm. As a file on disk, the Neflix Prize data (a matrix of about 480,000 members' ratings for about 18,000 movies) was about 65Gb in size -- too large to be read into the standard in-memory data model of  open-source R  directly. But in the video below, Brian Lewis shows us how to use the sparse Matrix object in R to efficiently store the data (about 99 million actual movie ratings) and the irlba package (which features a fast and efficient SVD algorithm for big data) to perform SVD analysis on the Netflix data in R.

blog.revolutionanalytics.com/2011/05/the-neflix-prize-big-data-svd-and-r.html

Big Computing: Bryan Lewis's Vignette on IRLBA for SVD in R