Hi Kris, ALS factorizes your input matrix A (users x items) into two smaller, dense matrices: U (users x features) and M (features x items).
Best, Sebastian On 13.11.2012 13:12, Kris Jack wrote: > What are the equations to estimate how much RAM is required for the mappers > and reducers in the ParallelALSFactorizationJob steps? > > I don't think that I'm alone in asking having this kind of question. It > would probably be useful to include such estimates in the documents and in > the code too as standard for jobs. > > Best, > Kris > > > > > > 2012/11/12 Sebastian Schelter <[email protected]> > >> Hi Kris, >> >> There is no such code. You can partially built that from what we have. >> You can use ItemSimilarityJob to compute item similarities, load the >> results into the non-distributed recommenders using a FileDataModel and >> do the evaluation there. >> >> Best, >> Sebastian >> >> On 12.11.2012 16:42, Kris Jack wrote: >>> Thanks guys, I've got that working now. I was interested to find that >>> there is code that helps to evaluate the results of AWS. Out of >> interest, >>> is there similar code in Mahout that helps with evaluating matrix >>> multiplication (e.g. >> org.apache.mahout.cf.taste.hadoop.item.RecommenderJob)? >>> >>> Best, >>> Kris >>> >>> >>> >>> >>> >>> 2012/10/18 Sebastian Schelter <[email protected]> >>> >>>>> I don't know if there is code, >>>>> probably not, but conceptually that is all that it involves. >>>> >>>> Once you factorized your interaction matrix, you can use >>>> >>>> org.apache.mahout.cf.taste.hadoop.als.RecommenderJob >>>> >>>> to compute recommendations in parallel. >>>> >>>> Best, >>>> Sebastian >>>> >>>> >>>> >>>> >>> >>> >> >> > >
