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https://issues.apache.org/jira/browse/SPARK-3066?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14389973#comment-14389973
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Debasish Das commented on SPARK-3066:
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Also unless the raw flow runs there is no way to validate how good a LSH based
flow is doing since users...I updated the PR today with [~mengxr] reviews...I
am working on level 3 BLAS routines for item->item similarity calculation from
matrix factors and the same optimization can be applied here...I will open up
the PR for that in coming weeks...we already have a JIRA for rowSimilarities...
> 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
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