spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-3066) Support recommendAll in matrix factorization model
Date Wed, 04 Mar 2015 23:06:38 GMT

    [ https://issues.apache.org/jira/browse/SPARK-3066?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14347742#comment-14347742
] 

Joseph K. Bradley commented on SPARK-3066:
------------------------------------------

Are there approximate methods which would be faster?  On single machines, there are data structures
for finding approximate nearest neighbors quickly.  I'm not sure about distributed data structures.

> Support recommendAll in matrix factorization model
> --------------------------------------------------
>
>                 Key: SPARK-3066
>                 URL: https://issues.apache.org/jira/browse/SPARK-3066
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Xiangrui Meng
>            Assignee: Debasish Das
>
> ALS returns a matrix factorization model, which we can use to predict ratings for individual
queries as well as small batches. In practice, users may want to compute top-k recommendations
offline for all users. It is very expensive but a common problem. We can do some optimization
like
> 1) collect one side (either user or product) and broadcast it as a matrix
> 2) use level-3 BLAS to compute inner products
> 3) use Utils.takeOrdered to find top-k



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org


Mime
View raw message