As far as I know the ALS algorithm is described in the paper:
Yunhong Zhou, Dennis Wilkinson, Robert Schreiber and Rong Pan. Large-Scale Parallel Collaborative Filtering for the Netflix Prize. Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management. Shanghai, China pp. 337-348, 2008. Best, Dr. Danny Bickson Project Scientist, Machine Learning Dept. Carnegie Mellon University On Mon, Dec 10, 2012 at 5:59 PM, Royi Ronen <[email protected]> wrote: > Hi, > > I am looking for confirmation regarding my usage of Mahout matrix > factorization with implicit feedback. > The input file is of the form <user,item,1> , as advised in one of the > Mahout forums. > All my usage points are positive (i.e, the user watched the movie). > > I changed the MovieLens Example: > > $MAHOUT parallelALS --input /tmp/mahout-work-cloudera/input.txt --output > ${WORK_DIR}/als/out \ > --tempDir ${WORK_DIR}/als/tmp --numFeatures 20 --numIterations 40 > --lambda 0.065 --implicitFeedback true > > # compute recommendations > $MAHOUT recommendfactorized --input ${WORK_DIR}/als/out/userRatings/ > --output ${WORK_DIR}/recommendations/ \ > --userFeatures ${WORK_DIR}/als/out/U/ --itemFeatures > ${WORK_DIR}/als/out/M/ \ > --numRecommendations 10 --maxRating 5 > > > This runs OK and gives recommendations that sometimes seem to be biased > towards popular items. > I would like to verify that this is the right way to run it. > > Also - does anyone know which algorithm is used to factorize? > > Thanks very much :) >
