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https://issues.apache.org/jira/browse/MAHOUT-1365?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dmitriy Lyubimov updated MAHOUT-1365:
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Attachment: distributed-als-with-confidence.pdf
Oh. the confidence matrix C is not sparse per se. but if there's a base
confidence c_0 such that subtracting it from each element of C turns it into
sparse matrix C', then we can use that matrix as an input (along with c_0
parameter). This is further clarified in the attachment (which is basically
just a conspect of both papers for my own sake.) See attached.
> 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: Backlog
>
> 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 followsing 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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