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https://issues.apache.org/jira/browse/MAHOUT-1272?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13701672#comment-13701672
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Peng Cheng commented on MAHOUT-1272:
------------------------------------
Hey honoured contributors I've got some crude test results for the new parallel
SGD factorizer for CF:
1. parameters:
lambda = 1e-10
rank of the rating matrix/number of features of each user/item vectors = 50
number of biases: 3 (average rating + user bias + item bias)
number of iterations/epochs = 2 (for all factorizers including ALSWR,
ratingSGD and the proposed parallelSGD)
initial mu/learning rate = 0.01 (for ratingSGD and proposed parallelSGD)
decay rate of mu = 1 (does not decay) (for ratingSGD and proposed
parallelSGD)
other parameters are set to default.
2. result on movielens-10m (I don't know what the hell happened to ALSWR, the
default hyperparameters must screw up real bad, but my point is the speed edge):
a. RMSE
Jul 07, 2013 5:20:23 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ALSWRFactorizer: 3.7709163950800665E21
time spent: 6.179s===================
Jul 07, 2013 5:20:23 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With RatingSGDFactorizer:
0.8847393972529887 time spent: 6.179s===================
Jul 07, 2013 5:20:23 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ParallelSGDFactorizer:
0.8805947464818478 time spent: 3.084s====================
b. Absolute Average
INFO: ==================Recommender With ALSWRFactorizer: 1.2085420449917682E19
time spent: 7.444s===================
Jul 07, 2013 5:22:39 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With RatingSGDFactorizer:
0.6757777685274206 time spent: 7.444s===================
Jul 07, 2013 5:22:39 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ParallelSGDFactorizer:
0.6775774766740665 time spent: 2.365s====================
3. result on movielens-1m (in average sgd works worse on it comparing to
movielens-10m, perhaps I could use more iterations/epochs)
a. RMSE
Jul 07, 2013 5:26:04 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ALSWRFactorizer: 1.3514189134383086E20
time spent: 0.637s===================
Jul 07, 2013 5:26:04 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With RatingSGDFactorizer:
0.9312989913558529 time spent: 0.637s===================
Jul 07, 2013 5:26:04 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ParallelSGDFactorizer:
0.9529995632658007 time spent: 0.305s====================
b. Absolute Average
Jul 07, 2013 5:25:29 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ALSWRFactorizer:
1.58934499216789965E18 time spent: 0.626s===================
Jul 07, 2013 5:25:29 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With RatingSGDFactorizer:
0.7459565635961599 time spent: 0.626s===================
Jul 07, 2013 5:25:29 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: ==================Recommender With ParallelSGDFactorizer:
0.7420818642753416 time spent: 0.297s====================
Great thanks to Sebastian for his guidance, I'll upload the EvaluatorRunner
class as a mahout-example component and the formatted code shortly.
> 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
> Attachments: mahout.patch, ParallelSGDFactorizer.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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