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https://issues.apache.org/jira/browse/SPARK-3066?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14351945#comment-14351945
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Debasish Das commented on SPARK-3066:
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[~josephkb] do you mean knn ? For recommendation until you do the dot product I
am not sure how can you find topk..level 3 BLAS will definite give a big boost
since it's all blocked dense with dense multiplication...For
https://issues.apache.org/jira/browse/SPARK-4823 I am looking into dense dense
BLAS and dense sparse BLAS..ideally there we can add in a knn based
optimization followed by row similarity calculation
> 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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