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https://issues.apache.org/jira/browse/MAHOUT-824?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13118947#comment-13118947
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Lance Norskog commented on MAHOUT-824:
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The new MemoryDiffStorage2 is revamped to use FastByIDRunningAverage. The
problem is that SlopeOneRecommender gives different RMS evaluation numbers on
real data. The unit tests match- I've added a bunch more unit tests trying to
track down the problem. Only with real data is there a problem.
I'm giving up for the nonce. SlopeOne is so much better that I still think it
is worth optimizing, but I've run out of steam.
FastByIDRunningAverage is tight and could be used in other places, so I've
packaged it as a separate smaller patch.
> FastByIDRunningAverage: Optimize SlopeOneRecommender by optimizing
> MemoryDiffStorage
> ------------------------------------------------------------------------------------
>
> Key: MAHOUT-824
> URL: https://issues.apache.org/jira/browse/MAHOUT-824
> Project: Mahout
> Issue Type: Improvement
> Reporter: Lance Norskog
> Priority: Trivial
> Attachments: MAHOUT-824.patch, MAHOUT-824.short.patch
>
>
> The SlopeOneRecommender has by far the best RMS of all of the online
> recommenders in Mahout (that I've found). Unfortunately the implementation
> also uses much more memory and is unuseable on my laptop.
> This patch optimizes memory (and speed) by folding
> FastByIDMap<RunningAverage> into one class: FastByIDRunningAverage. This is
> what it sounds like: a Long-addressable array of running averages (and
> optionally standard deviation).
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