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https://issues.apache.org/jira/browse/SPARK-13857?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15237001#comment-15237001
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Sean Owen commented on SPARK-13857:
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Yeah the semantics of a recommender are different from a simple supervised
learning problem. There are two key operations: recommend items to users, make
a point estimate for a user-item pair. (Recommending users to items is
analogous.) These require different prediction and evaluation semantics. To
make it work the behavior must vary according to the structure of the input DF.
If the DF only has a user column, then output a recommended item column
containing a list of item IDs. If the DF has a user and item column, output a
estimated rating/strength column. This then implies that for evaluation, the
input DF has to have these output columns, respectively, for comparison.
I think this is just restating what's above, but is this possible and then is
this not the most direct way to solve this?
> 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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