Mahout also has a map-reduce stochastic projection SVD. On Thu, May 10, 2012 at 8:45 AM, Sebastian Schelter <[email protected]> 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 >
