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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: About Matrix Factorization and Vector/Matrix Manipulation
Date Fri, 11 May 2012 06:09:51 GMT
Mahout also has a map-reduce stochastic projection SVD.

On Thu, May 10, 2012 at 8:45 AM, Sebastian Schelter <ssc@apache.org> wrote:

> On 10.05.2012 17:33, 冯伟 wrote:
> > 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?
>
> Mahout's implementation is based on the following papers:
>
> Large-scale Parallel Collaborative Filtering for the Netflix Prize
>
> http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf
>
> Collaborative Filtering for Implicit Feedback Datasets
> http://research.yahoo.com/pub/2433
>
> There is a comparison in the original Graphlab paper which is a little
> biased IMHO because it uses an initial hacky version of the ALS
> implementation and the experiment is run on a really small dataset.
>
> I still think that Mahout's implementation will be something like 20x
> slower than GraphLab mainly due to Hadoop's inability to efficiently run
> iterative computations.
>
> Mahout only has a non-distributed SGD implementation of matrix
> factorization.
>
> --sebastian
>

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