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https://issues.apache.org/jira/browse/SPARK-6407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15896177#comment-15896177
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Daniel Li edited comment on SPARK-6407 at 3/5/17 10:57 AM:
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{quote}
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.
{quote}
Hmm, I see. This would be something I'd be interested in implementing for
Spark if there's need. Are there implementations (or papers) of this you know
of that I could look at?
was (Author: danielyli):
bq. 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.
Hmm, I see. This would be something I'd be interested in implementing for
Spark if there's need. Are there implementations (or papers) of this you know
of that I could look at?
> 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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