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https://issues.apache.org/jira/browse/SPARK-6407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14392742#comment-14392742
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Sean Owen commented on SPARK-6407:
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ALS doesn't use gradient descent, at least not enough in the sense that these
linear models do that you could reuse the implementation. I am accustomed to
fold-in for approximate streaming updates to an ALS model, but yes it does kind
of need to mutate some RDD-based data structured efficiently like an
IndexedRDD. Although the idea is simple I also don't know of good theoretical
approaches and have just made up reasonable heuristics in the past.
> Streaming ALS for Collaborative Filtering
> -----------------------------------------
>
> Key: SPARK-6407
> URL: https://issues.apache.org/jira/browse/SPARK-6407
> Project: Spark
> Issue Type: New Feature
> Components: Streaming
> 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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