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From "Tamas Jambor (JIRA)" <>
Subject [jira] Commented: (MAHOUT-541) Incremental SVD Implementation
Date Sat, 05 Mar 2011 16:32:45 GMT


Tamas Jambor commented on MAHOUT-541:


I got around to do the test with movielens 10m (64 features and 20 iterations).

Training time mahout: 23185168ms
Training time this one: 4271031ms

> Incremental SVD Implementation
> ------------------------------
>                 Key: MAHOUT-541
>                 URL:
>             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,,,
> 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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