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https://issues.apache.org/jira/browse/SPARK-4675?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14241952#comment-14241952
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Joseph K. Bradley commented on SPARK-4675:
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Just to make sure I get your last question, are you asking, "Why compute
product similarities using the low-dimensional space when we could do it in the
high-dimensional space?" If so, then my understanding is that the
low-dimensional space will give more meaningful similarities in general.
> Find similar products and similar users in MatrixFactorizationModel
> -------------------------------------------------------------------
>
> Key: SPARK-4675
> URL: https://issues.apache.org/jira/browse/SPARK-4675
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Steven Bourke
> Priority: Trivial
> Labels: mllib, recommender
>
> Using the latent feature space that is learnt in MatrixFactorizationModel, I
> have added 2 new functions to find similar products and similar users. A user
> of the API can for example pass a product ID, and get the closest products.
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