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https://issues.apache.org/jira/browse/SPARK-6407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14423680#comment-14423680
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Xiangrui Meng commented on SPARK-6407:
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Using ALS for online updates is expensive. I think we should use the factors
from ALS as the initial point and use a stochastic gradient descent scheme for
online update, e.g. DSGD: http://dl.acm.org/citation.cfm?id=2020426. I'm not
sure whether this would work. Someone should work out the math first.
> 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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