I want to look at the distribution implementation of matrix factorization
in Mahout Recommender System. Before I start from
org.apache.mahout.cf.taste.hadoop.als.RecommenderJob，is there any papers /
technical materials for reference? It seems that the parameters are learned
by ALS. Then is there a stochastic gradient descent implementation? I know
GraphLab of CMU for quite a while since KDDCup 2011，is there any comparison
between GraphLab's collaborative filtering lib and Mahout's?
The last question is about vector/matrix manipulation. In Matlab / Octave,
algorithms that employ vector/matrix manipulation are always faster than
their nonvectorized version (iterating elements in vectors and matrix one
by one). It seems that Matlab has employed the some underlining hardware
supported technique (http://en.wikipedia.org/wiki/General_Matrix_Multiply).
Is this technique supported by Mahout too ? Especially for sparse
vector/matrix.

Wei Feng
