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 <ronen.r...@gmail.com> 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 :)
>>
>
