[
https://issues.apache.org/jira/browse/SPARK-6407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14481874#comment-14481874
]
Burak Yavuz commented on SPARK-6407:
------------------------------------
I actually worked on this over the weekend for fun and have a streaming,
gradient descent based, matrix factorization model implemented here:
https://github.com/brkyvz/streaming-matrix-factorization
It is a very naive implementation, but it might be something to work on top of.
I will publish a Spark Package for it as soon as I get the tests in. The model
it uses for predicting ratings for user `u` and product `p` is:
{code}
r = U(u) * P^T(p) + bu(u) + bp(p) + mu
{code}
where U(u) is the u'th row of the User matrix, P(p) is the p'th row for the
product matrix, bu(u) is the u'th element of the user bias vector, bp(p) is the
p'th element of the product bias vector and mu is the global average.
> 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
