[
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?
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
