# Matrix factorization translation

I am trying to implement matrix factorization. My dataset is 3-dimensional and the size is (500x10x5). The standard approach in Netflix comptetition, contains users ratings (in a 2D matrix M). The appproach decompose this 2D matrix to U, V in the equation M = UV where U represents the latent factor of the users while the V represents the latent factors of the items-movies. During my research I came across that those latent factors represents the movies-genres, so there is a possibility to cluster items to genres after the decomposition and propose to some user movies from the same cluster. I am trying to understand how this interpretation happerning and how can I do the analogous interpration with different data. Can I group instead of items to genres, users to type of users? In fact what I am trying to figure out is if it is possible to group users using matrix factorization!

• what's your question? are you asking algorithms on tensor ("500*10*5" /3D matrix) or asking the interpretation of matrix factorization? – Haitao Du Sep 2 '16 at 14:07
• My question is the interpration of the Tensor basically. – Jose Ramon Sep 12 '16 at 15:23