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https://issues.apache.org/jira/browse/MAHOUT-824?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13119116#comment-13119116
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Lance Norskog commented on MAHOUT-824:
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Sure, this is fine.
MemoryDiffStorage exposes the fact that it uses the RunningAverage(AndStdDev)
classes. This was a vexing implementation leakage, and is why I had to add new
constructors for FullRunningAverage(AndStdDev). If I went after it more, I
would correct this.
Anyway- is there a paper somewhere that defines the error loss for the
inconsequential diff pruning technique? Is there a better subsampler than 'stop
after the first N entries'?
> 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
> Assignee: Sean Owen
> Priority: Trivial
> Fix For: 0.6
>
> 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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