Pat Ferrel resolved MAHOUT-1883.
    Resolution: Fixed

Hmm, I thought these were aut-resolved with a commit that contains the issue 
name? Maybe I had a senior moment there :-)

> Create a type if IndexedDataset that filters unneeded data for CCO
> ------------------------------------------------------------------
>                 Key: MAHOUT-1883
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1883
>             Project: Mahout
>          Issue Type: New Feature
>          Components: Collaborative Filtering
>    Affects Versions: 0.13.0
>            Reporter: Pat Ferrel
>            Assignee: Pat Ferrel
>             Fix For: 0.13.0
> 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.

This message was sent by Atlassian JIRA

Reply via email to