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From "Tamas Jambor (JIRA)" <j...@apache.org>
Subject [jira] Updated: (MAHOUT-541) Incremental SVD Implementation
Date Wed, 22 Dec 2010 07:30:01 GMT

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

Tamas Jambor updated MAHOUT-541:
--------------------------------

    Attachment: MAHOUT-541.patch

> 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, 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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