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https://issues.apache.org/jira/browse/MAHOUT-541?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12970648#action_12970648
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Tamas Jambor commented on MAHOUT-541:
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Hi Sebastian,
sorry, I am not really familiar with how this patch thing works.
yes, it is based on the link you suggested.
as far as I remember I changed the following methods:
public void train(int steps) in TJSVDRecommender
and added the predictRating(int i, int j, int f, Preference pref, boolean
bTrailing) method in TJExpecationMaximization
> 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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