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https://issues.apache.org/jira/browse/SPARK-4675?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14241535#comment-14241535
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Debasish Das commented on SPARK-4675:
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There are few issues:
1. Batch API for topK similar users and topK similar products
2. Comparison of product x product similarities generated with
columnSimilarities and compared with topK similar products
I added batch APIs for topK product recommendation for each user and topK user
recommendation for each product in SPARK-4231...similar batch API will be very
helpful for topK similar users and topK similar products...
I agree with Cosine Similarity...you should be able to re-use column similarity
calculations...I think a better idea is to add rowMatrix.similarRows and re-use
that code to generate product similarities and user similarities...
But my question is more on validation. We can compute product similarities on
raw features and we can compute product similarities on matrix product
factor...which one is better ?
> 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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