[
https://issues.apache.org/jira/browse/SPARK-6407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15896154#comment-15896154
]
Sean Owen commented on SPARK-6407:
----------------------------------
Computing one or two iterations per update -- as in every time someone clicks
on a product or something? no, that's way way too slow. Each would launch tens
of large distributed jobs.
In practice fold-in works fine. Folding in a day or so of updates has been OK.
The question isn't RMSE but how it affects actual rankings of items in
recommendations, and it takes a while before the effect of the approximation
actually changes a rank.
> 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?
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
