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https://issues.apache.org/jira/browse/MAHOUT-541?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12974099#action_12974099
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Tamas Jambor commented on MAHOUT-541:
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sorry for the late reply.
I had a look at the code, it seems good. I made some slight modifications,
attached the patch.
I don't know what was the reason of storing the a modified datamodel with the
predicted rating, it was implemented in mahout, but later removed, as far as I
remember.
I have tested the speedup, and it was much faster with the 1m movielens. If you
have a look at the code there are way less calculations needed (especially
calculating the dot product of the matrix for prediction during training), I
can run that to confirm.
> 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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