[
https://issues.apache.org/jira/browse/SPARK-13857?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15195702#comment-15195702
]
Nick Pentreath edited comment on SPARK-13857 at 3/16/16 6:42 AM:
-----------------------------------------------------------------
Also, what's nice in the ML API is that SPARK-10802 is essentially taken care
of by passing in a DataFrame with the users of interest, e.g.
{code}
val users = df.filter(df("age") > 21)
val topK = model.setK(10).setUserTopKCol("userTopK").transform(users)
{code}
was (Author: mlnick):
Also, what's nice in the ML API is that SPARK-10802 is essentially taken care
of by passing in a DataFrame with the users of interest, e.g.
{code}
val users = df.filter(df("age") > 21)
val topK = model.setK(10).setTopKCol("userId").transform(users)
{code}
> Feature parity for ALS ML with MLLIB
> ------------------------------------
>
> Key: SPARK-13857
> URL: https://issues.apache.org/jira/browse/SPARK-13857
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: 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).
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
