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https://issues.apache.org/jira/browse/MAHOUT-541?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12974104#action_12974104
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Sebastian Schelter commented on MAHOUT-541:
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No need to excuse for a late reply, we're all doing this in our spare time,
thank you for looking through the patch.
Can you give us some numbers on the speed increase or describe your test setup
again? I did some simple testing too and didn't see it run faster, but maybe I
just got something wrong. In my understanding the code's intention is to trade
a higher memory usage (the cached values) for a potential speed increase (not
having to do calculate the dot-products), did I get that correctly?
> Incremental SVD Implementation
> ------------------------------
>
> Key: MAHOUT-541
> URL: https://issues.apache.org/jira/browse/MAHOUT-541
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.4
> Reporter: Tamas Jambor
> Assignee: Sean Owen
> Fix For: 0.5
>
> Attachments: MAHOUT-541.patch, MAHOUT-541.patch, SVDPreference.java,
> TJExpectationMaximizationSVD.java, TJSVDRecommender.java
>
>
> I thought I'd put up this implementation of the popular SVD algorithm for
> recommender systems. It is based on the SVD implementation, but instead of
> computing each user and each item matrix, it trains the model iteratively,
> which was the original version that Simon Funk proposed. The advantage of
> this implementation is that you don't have to recalculate the dot product of
> each user-item pair for each training cycle, they can be cached, which speeds
> up the algorithm considerably.
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