spark-issues mailing list archives

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


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:
>             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
> 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

To unsubscribe, e-mail:
For additional commands, e-mail:

View raw message