Details

    • Type: Sub-task
    • Status: Closed
    • Priority: Major
    • Resolution: Duplicate
    • Affects Version/s: None
    • Fix Version/s: None
    • Component/s: ML
    • Labels:
      None

      Description

      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).

        Attachments

          Issue Links

            Activity

              People

              • Assignee:
                mlnick Nick Pentreath
                Reporter:
                mlnick Nick Pentreath
              • Votes:
                2 Vote for this issue
                Watchers:
                16 Start watching this issue

                Dates

                • Created:
                  Updated:
                  Resolved: