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https://issues.apache.org/jira/browse/SPARK-3066?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14209936#comment-14209936
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Debasish Das commented on SPARK-3066:
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On our internal datasets, flatMap is slow...I am changing the code to have 2
methods (assuming users are tall and products are skinny)...if user and product
are tall and wide then we need to rethink
recommendAllUsers: takeOrdered is called on each userFeature dot productFeatures
recommendAllProducts: mapPartitions will emit Iterator(productId,
userPriorityQueue) and reduceByKey will generate the topK users for each
product..
> 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
>
> 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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