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https://issues.apache.org/jira/browse/MAHOUT-737?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sebastian Schelter updated MAHOUT-737:
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Attachment: MAHOUT-737-2.patch
I refactored the patch to work with our math code.
Instead of explicitly computing x = A^-1 y you can simply solve Ax = y which is
offered by our QRDecomposition class. No need to explictly compute inverses.
I think some calculations in the code could still be optimized, but I didn't
want to change too much in this first step.
Tamas, can you have a look at my changes? Do you have an idea how we could test
this implementation?
It would also be great if you could point me to the paper this algorithm is
based on. Is it "Collaborative Filtering for Implicit Feedback Datasets"
http://research.yahoo.com/pub/2433 ?
> Implicit Alternating Least Squares SVD
> --------------------------------------
>
> Key: MAHOUT-737
> URL: https://issues.apache.org/jira/browse/MAHOUT-737
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Affects Versions: 0.6
> Reporter: Tamas Jambor
> Assignee: Sebastian Schelter
> Attachments: MAHOUT-737-2.patch, MAHOUT-737.patch, MAHOUT-737.patch,
> MAHOUT-737.patch, MAHOUT-737.patch
>
>
> I am sharing this Java implementation of mine that is based on the paper -
> Collaborative Filtering with Implicit Datasets. The implementation is
> multi-treading and can be easily extended to use it on Hadoop. In fact this
> approach would possibly work with non-implicit datasets, but further testing
> is needed. The algorithm is tried and tested on an implicit TV-viewing
> dataset, and the performance was pretty good (details to follow).
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