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https://issues.apache.org/jira/browse/MAHOUT-1365?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13906747#comment-13906747
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Sean Owen commented on MAHOUT-1365:
-----------------------------------
It's the exact same implementation you are talking about, as far as I
understand what you're describing. It's just the standard paper algorithm. The
input are 'preferences' that are translated to confidences with 1 + a*R. You
can attach any confidence to any 0/1. It will also weight towards 0, not 1, if
input value is negative (my own touch).
> Weighted ALS-WR iterator for Spark
> ----------------------------------
>
> Key: MAHOUT-1365
> URL: https://issues.apache.org/jira/browse/MAHOUT-1365
> Project: Mahout
> Issue Type: Task
> Reporter: Dmitriy Lyubimov
> Assignee: Dmitriy Lyubimov
> Fix For: 1.0
>
> Attachments: distributed-als-with-confidence.pdf
>
>
> Given preference P and confidence C distributed sparse matrices, compute
> ALS-WR solution for implicit feedback (Spark Bagel version).
> Following Hu-Koren-Volynsky method (stripping off any concrete methodology to
> build C matrix), with parameterized test for convergence.
> The computational scheme is following ALS-WR method (which should be slightly
> more efficient for sparser inputs).
> The best performance will be achieved if non-sparse anomalies prefilitered
> (eliminated) (such as an anomalously active user which doesn't represent
> typical user anyway).
> the work is going here
> https://github.com/dlyubimov/mahout-commits/tree/dev-0.9.x-scala. I am
> porting away our (A1) implementation so there are a few issues associated
> with that.
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