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https://issues.apache.org/jira/browse/MAHOUT-1422?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13965033#comment-13965033
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Sebastian Schelter commented on MAHOUT-1422:
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That is great, because it means that we can reuse the down-sampling code from
MAHOUT-1464 for the cross-co-occurrence case. One last question, how do I get
the four counts for LLR (X & Y, X but not Y, Y but not X, neither of them) in
the cross-co-occurrence case?
Apparently X & Y is what I get from A'B, how do I get the others?
> Make a version of RSJ that uses two inputs
> ------------------------------------------
>
> Key: MAHOUT-1422
> URL: https://issues.apache.org/jira/browse/MAHOUT-1422
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 1.0
> Environment: mapreduce
> Reporter: Pat Ferrel
> Labels: recommender, similarity
> Fix For: 1.0
>
>
> Currently the RowSimiairtyJob uses a similarity measure to pairwise compare
> all rows in a DistributedRowMatrix.
> For many applications including a cross-action recommender we need something
> like RSJ that takes two DRMs and compares matching rows of each. The output
> would be the same form as RSJ, and ideally would allow the use of any
> similarity type already defined--especially LLR.
> There are two implementations of a Cross-Recommender one based on the Mahout
> RecommenderJob, and another based on Solr, that can immediately benefit from
> a Cross-RSJ.
> A modification of the matrix multiply job may be a place to start since the
> current RSJ seems to rely heavily if self-similarity.
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