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https://issues.apache.org/jira/browse/MAHOUT-1272?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Peng Cheng updated MAHOUT-1272:
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Labels: features patch test (was: )
Status: Patch Available (was: Open)
Hey I have finished the class and test for parallel sgd factorizer for
matrix-completion based recommender (not mapreduced, just single machine
multi-thread), it is loosely based on vanilla sgd and hogwild!. I have only
tested on toy and synthetic data (2000users * 1000 times) but it is pretty
fast, 3-5x times faster than vanilla sgd with 8 cores. (never exceed 6x,
apparently the executor induces high overhead allocation cost) And definitely
faster than single machine ALSWR.
I'm submitting my java files and patch for review.
> 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: patch, test, features
> 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 high-performance online recommender as a replacement of the outdated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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