Difference between revisions of "10-601 Matrix Factorization"

Revision as of 13:37, 13 April 2016

Matrix factorization and collaborative filtering is not covered in Murphy or Mitchell. Some external readings are below.

Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 8 (2009): 30-37.
There's a nice description of the gradient-based approach to MF, and a scheme for parallelizing it,by Gemulla et al.

You should know:

What loss function and constraints are associated with PCA - i.e., what the "PCA optimization problem" is.
How to interpret the low-dimensional embedding of instances, and the "prototypes" produced by PCA and MF techniques.
How to interpret the prototypes in the case of dimension reduction for images.

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 === Readings ===
-* Murphy Chap 12; Bishop chapter 12. (PCA is not covered in Mitchell. )
-* There are also some [http://www.cs.cmu.edu/~wcohen/10-601/PCA-notes/pca.pdf notes on PCA/SVD] that I've written up.
+Matrix factorization and collaborative filtering is not covered in Murphy or Mitchell.  Some external readings are below.
-* There's a [http://arxiv.org/abs/1404.1100 good tutorial introduction to PCA] on arxiv.
+* Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 8 (2009): 30-37.
 * There's a nice description of [http://people.mpi-inf.mpg.de/~rgemulla/publications/rj10481rev.pdf the gradient-based approach to MF], and a scheme for parallelizing it,by Gemulla et al.