[jira] Commented: (MAHOUT-541) Incremental SVD Implementation

["Sebastian Schelter (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-541?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12970642#action_12970642 ] 

Sebastian Schelter commented on MAHOUT-541:
-------------------------------------------

Hi Tamas,

I'd like to review this, but that's kinda difficult because you did not supply a patch from which your changes can be seen. I'm looking through the code and getting some idea about the improvements you've made, yet it would be very helpful if you could point me to the main code passages you've changed.

Are you refering to http://sifter.org/~simon/journal/20061211.html when talking about the original version of the algorithm?

> 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: 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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