The Netflix Prize, Big Data, SVD and R

[This article was first published on Revolutions, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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.

 

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

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)