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https://issues.apache.org/jira/browse/MAHOUT-1004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13265749#comment-13265749
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Sean Owen commented on MAHOUT-1004:
-----------------------------------
It sounds like a fine idea. The ideal thing would be some fancy, refactored
approach that didn't duplicate much of anything. The next-best approach would
be a cleanly parallel implementation. Next-best would be a different approach.
I imagine the first is not realistic, and I imagine the last is probably not a
great thing for the project. Kris is it mostly a parallel implementation? or
could you also push back any of your improvements to the .item implementation?
With that and avoiding any easily-avoided duplication I bet this would be fine.
> Distributed User-based Collaborative Filtering
> ----------------------------------------------
>
> Key: MAHOUT-1004
> URL: https://issues.apache.org/jira/browse/MAHOUT-1004
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Reporter: Kris Jack
> Assignee: Sean Owen
> Priority: Minor
> Labels: Recommender, User-based
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> I'd like to contribute code that implements a distributed user-based
> collaborative filtering algorithm.
> In brief, so far I've taken the code for the existing
> org.apache.mahout.cf.taste.hadoop.item.RecommenderJob and created a new
> org.apache.mahout.cf.taste.hadoop.user.RecommenderJob. With help from Sean
> Owen, I followed a similar approach to the item-based implementation, but
> multiplied a user-user matrix with a user-item vector rather than an
> item-item matrix with an item-user vector. The result of the multiplication
> then needs to be transposed in order to output recommendations by user id.
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