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https://issues.apache.org/jira/browse/SPARK-4675?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14243026#comment-14243026
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Debasish Das commented on SPARK-4675:
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Is there a metric like MAP / AUC kind of measure that can help us validate
similarUsers and similarProducts ?
Right now if I run column similarities with sparse vector on matrix
factorization datasets for product similarities, it will assume all unvisited
entries (which should be ?) as 0 and compute column similarities for...If the
sparse vector has ? in place of 0 then basically all similarity calculation is
incorrect...so in that sense it makes more sense to compute the similarities on
the matrix factors...
But then we are back to map-reduce calculation of rowSimilarities.
> 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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