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https://issues.apache.org/jira/browse/MAHOUT-1422?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13908579#comment-13908579
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Pat Ferrel commented on MAHOUT-1422:
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There is another job that needs to be created for the cross-recommender, this
job could take any number of inputs but I believe would use the XRSJ in pairs
internally. I did a prototype that can use 3 actions on the same items by the
same users. It does matrix multiply for cooccurrence similarity in pairs as
described above.
Haven't entered that into Jira yet
> 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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