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https://issues.apache.org/jira/browse/MAHOUT-1365?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13906729#comment-13906729
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Dmitriy Lyubimov commented on MAHOUT-1365:
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Oh. and the implicit paper doesn't generalize the search for confidence
parameters of course. I ignore that formulation here completely. but eventually
there should be an outer procedure for search for optimum. My particular
problem was including multiple events with generally unknown confidence weights
unlike the original implicit feedback work.
> 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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