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Nick Pentreath commented on SPARK-13857: ---------------------------------------- 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