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https://issues.apache.org/jira/browse/MAHOUT-541?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sebastian Schelter updated MAHOUT-541:
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Attachment: MAHOUT-541.patch
Tamas,
I created this patch from the files you supplied, and I also cleaned up the
code a little. I did some simple testing and the recommender seems to work fine.
I left out something because I did not understand it: You use a "modified"
dataModel after training where the original preferences are replaced by the
estimated ones, what's the reason for doing this?
Another question: How can we test the speedup this patch should bring? I did
some evaluation on the 1M movielens dataset and didn't see any increase in
computation speed, but maybe that dataset is too small or I got the parameters
wrong.
Can you please review the patch and see if I got everything right?
> 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, 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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