The collaborative filtering CCO algo uses drms for each "indicator" type. The input must have the same set of user-id and so the row rank for all input matrices must be the same.
In the past we have padded the row-id dictionary to include new rows only in secondary matrices. This can lead to very large amounts of data processed in the CCO pipeline that does not affect the results. Put another way if the row doesn't exist in the primary matrix, there will be no cross-occurrence in the other calculated cooccurrences matrix.
if we are calculating P'P and P'S, S will not need rows that don't exist in P so this Jira is to create an IndexedDataset companion object that takes an RDD[(String, String)] of interactions but that uses the dictionary from P for row-ids and filters out all data that doesn't correspond to P. The companion object will create the row-ids dictionary if it is not passed in, and use it to filter if it is passed in.
We have seen data that can be reduced by many orders of magnitude using this technique. This could be handled outside of Mahout but always produces better performance and so this version of data-prep seems worth including.
It does not affect the CLI version yet but could be included there in a future Jira.