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https://issues.apache.org/jira/browse/SPARK-6407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15895605#comment-15895605
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Sean Owen commented on SPARK-6407:
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How is it different from recomputing all of U and V?
Doing anything to all of the matrices is probably out of the question for an
online update.
The point of fold-in is to update only the two affected rows and make the
simplifying assumption that nothing else changes, because it would be too
expensive to recompute anything.
If you mean batch together enough to make it worthwhile to update, then yes at
some point that's worth it, but it just reduces to re-running the batch
algorithm for a few iterations again.
> Streaming ALS for Collaborative Filtering
> -----------------------------------------
>
> Key: SPARK-6407
> URL: https://issues.apache.org/jira/browse/SPARK-6407
> Project: Spark
> Issue Type: New Feature
> Components: DStreams
> Reporter: Felix Cheung
> Priority: Minor
>
> Like MLLib's ALS implementation for recommendation, and applying to streaming.
> Similar to streaming linear regression, logistic regression, could we apply
> gradient updates to batches of data and reuse existing MLLib implementation?
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