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https://issues.apache.org/jira/browse/MAHOUT-1272?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13707830#comment-13707830
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Peng Cheng edited comment on MAHOUT-1272 at 7/13/13 8:57 PM:
-------------------------------------------------------------
Test on libimseti dataset (http://www.occamslab.com/petricek/data/), libimseti
is a czech dating website.
This dataset has been used in a live example described in book 'Mahout in
Action', page 71, written by a few guys hanging around this site.
parameters:
private final static double lambda = 0.1;
private final static int rank = 16;
private static int numALSIterations=5;
private static int numEpochs=20;
(for ratingSGD)
double randomNoise=0.02;
double learningRate=0.01;
double learningDecayRate=1;
(for parallelSGD)
double mu0=1;
double decayFactor=1;
int stepOffset=100;
double forgettingExponent=-1;
result (using average absolute difference, the rating is based on a 1-10 scale):
INFO: ==================Recommender With ALSWRFactorizer: 1.5623366369454739
time spent: 41.24s=================== (should be noted the number of ALS
iteration is much smaller than others, which leads to suboptimal result, but
this is not the point of this test)
Jul 13, 2013 4:39:34 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With RatingSGDFactorizer: 1.28022379922957
time spent: 118.188s===================
Jul 13, 2013 4:39:34 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ParallelSGDFactorizer:
1.2798905733917445 time spent: 21.806s====================
This is already the best result I can get, the original book claims a best
result of 1.12 on this dataset, which I never achieve. If you have also
experimented and find a better parameter set, please post here.
was (Author: peng):
Test on libimseti dataset (http://www.occamslab.com/petricek/data/),
libimseti is a czech dating website.
This dataset has been used in a live example described in book 'Mahout in
Action', page 71, written by a few guys hanging around this site.
parameters:
private final static double lambda = 0.1;
private final static int rank = 16;
private static int numALSIterations=5;
private static int numEpochs=20;
double randomNoise=0.02;
double learningRate=0.01;
double learningDecayRate=1;
result (using average absolute difference, the rating is based on a 1-10 scale):
INFO: ==================Recommender With ALSWRFactorizer: 1.5623366369454739
time spent: 41.24s=================== (should be noted the number of ALS
iteration is much smaller than others, which leads to suboptimal result, but
this is not the point of this test)
Jul 13, 2013 4:39:34 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With RatingSGDFactorizer: 1.28022379922957
time spent: 118.188s===================
Jul 13, 2013 4:39:34 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ParallelSGDFactorizer:
1.2798905733917445 time spent: 21.806s====================
This is already the best result I can get, the original book claims a best
result of 1.12 on this dataset, which I never achieve. If you have also
experimented and find a better parameter set, please post here.
> Parallel SGD matrix factorizer for SVDrecommender
> -------------------------------------------------
>
> Key: MAHOUT-1272
> URL: https://issues.apache.org/jira/browse/MAHOUT-1272
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Reporter: Peng Cheng
> Assignee: Sean Owen
> Labels: features, patch, test
> Fix For: 0.8
>
> Attachments: GroupLensSVDRecomenderEvaluatorRunner.java,
> libimsetiSVDRecomenderEvaluatorRunner.java, mahout.patch,
> ParallelSGDFactorizer.java, ParallelSGDFactorizer.java,
> ParallelSGDFactorizerTest.java, ParallelSGDFactorizerTest.java
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> a parallel factorizer based on MAHOUT-1089 may achieve better performance on
> multicore processor.
> existing code is single-thread and perhaps may still be outperformed by the
> default ALS-WR.
> In addition, its hardcoded online-to-batch-conversion prevents it to be used
> by an online recommender. An online SGD implementation may help build
> high-performance online recommender as a replacement of the outdated
> slope-one.
> The new factorizer can implement either DSGD
> (http://www.mpi-inf.mpg.de/~rgemulla/publications/gemulla11dsgd.pdf) or
> hogwild! (www.cs.wisc.edu/~brecht/papers/hogwildTR.pdf).
> Related discussion has been carried on for a while but remain inconclusive:
> http://web.archiveorange.com/archive/v/z6zxQUSahofuPKEzZkzl
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