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https://issues.apache.org/jira/browse/MAHOUT-1365?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13906741#comment-13906741
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Dmitriy Lyubimov commented on MAHOUT-1365:
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Yeah. I am not sure what they are doing there. Last time i looked at it, MLLib
did not have any form of weighed ALS. Now this exapmple seems to include
"trainImplicit" which works on the rating matrix only. In original formulation
of implicit feedback problem there were two values, preference and confidence
in such preference. So i am not sure what they do there since the input is
obviously one sparse matrix.
My generalization of the problem includes formulation where any confidence
level could be attached to either 0 or 1 as a preference, plus baseline. I also
assume that model may have more than one parameter to form confidence which
requires fitting as well. (simply speaking what is "level of consumption" if
user clicks on it vs. add-2-cart, if any etc.). Similarly, there could be
difference levels of confidence of ignoring stuff depending on situation. So 0
preferences do not have to always have the baseline confidence either.
> 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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