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Posted to dev@mahout.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2011/03/08 09:59:59 UTC

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

     [ https://issues.apache.org/jira/browse/MAHOUT-541?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved MAHOUT-541.
------------------------------

    Resolution: Fixed

> 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, 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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