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https://issues.apache.org/jira/browse/SPARK-13857?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15227126#comment-15227126
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Joseph K. Bradley edited comment on SPARK-13857 at 4/5/16 9:04 PM:
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I'd prefer to have a consistent schema for a given output column.
I think it would be hard to extend transform() since the number of rows may not
match. If transform() is outputting 1 row per training/test instance (a (user,
item) pair), then it cannot also output 1 row per user or 1 row per item.
I'd prefer to add recommendItems, recommendUsers methods for now. If a user
has a need for them in a Pipeline, we could later add support within
transform(). I haven't yet thought through how this would interact with model
selection/evaluation though.
How does that sound?
was (Author: josephkb):
I'd prefer to have a consistent schema for a given output column.
I think it would be hard to extend transform() since the number of rows may not
match. If transform() is outputting 1 row per training/test instance (a (user,
item) pair), then it cannot also output 1 row per user or 1 row per item.
I'd prefer to add recommendItems, recommendUsers methods for now. If a user
has a need for them in a Pipeline, we could later add support within
transform().
How does that sound?
> Feature parity for ALS ML with MLLIB
> ------------------------------------
>
> Key: SPARK-13857
> URL: https://issues.apache.org/jira/browse/SPARK-13857
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Nick Pentreath
> Assignee: Nick Pentreath
>
> Currently {{mllib.recommendation.MatrixFactorizationModel}} has methods
> {{recommendProducts/recommendUsers}} for recommending top K to a given user /
> item, as well as {{recommendProductsForUsers/recommendUsersForProducts}} to
> recommend top K across all users/items.
> Additionally, SPARK-10802 is for adding the ability to do
> {{recommendProductsForUsers}} for a subset of users (or vice versa).
> Look at exposing or porting (as appropriate) these methods to ALS in ML.
> Investigate if efficiency can be improved at the same time (see SPARK-11968).
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