The usage seems to be ok, I'm not sure whether the learning rate value (lambda) works well for the implicit variant of the algorithm, though.
The algorithm should work with binary data, but was originally designed to incorporate the strength of the implicit interaction (like number of views etc). /s On 10.12.2012 17:27, ronen.royi wrote: > > Thanks! Could you confirm the correcrness of usage? > > > > Sent from Samsung MobileSebastian Schelter <[email protected]> wrote:Hi > Royi, > > If you specify implicitFeedback=true, then another variant of ALS is > used that is described in this paper: > > Collaborative Filtering for Implicit Feedback Datasets > www2.research.att.com/~yifanhu/PUB/cf.pdf > > /s > > On 10.12.2012 17:07, Danny Bickson wrote: >> 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 :) >>> >> >
