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


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:
I'm not sure whether this would work. Someone should work out the math first.

> Streaming ALS for Collaborative Filtering
> -----------------------------------------
>                 Key: SPARK-6407
>                 URL:
>             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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