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Nick Pentreath commented on SPARK-13857: ---------------------------------------- My view is in practice brute-force is never going to be efficient enough for any non-trivial size problem. So I'd definitely like to incorporate the ANN stuff into the top-k recommendation here. Once SignRandomProjection is in I'll take a deeper look at item-item / user-user sim for DF-API. We could also add a form of LSH appropriate for dot product space for user-item recs. > 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). -- 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