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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-6407) Streaming ALS for Collaborative Filtering
Date Sun, 05 Apr 2015 22:28:06 GMT

    [ https://issues.apache.org/jira/browse/SPARK-6407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14423680#comment-14423680
] 

Xiangrui Meng commented on SPARK-6407:
--------------------------------------

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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