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  1. Spark
  2. SPARK-3066

Support recommendAll in matrix factorization model

    Details

    • Type: New Feature
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: None
    • Fix Version/s: 1.4.0
    • Component/s: MLlib
    • Labels:
      None
    • Target Version/s:

      Description

      ALS returns a matrix factorization model, which we can use to predict ratings for individual queries as well as small batches. In practice, users may want to compute top-k recommendations offline for all users. It is very expensive but a common problem. We can do some optimization like

      1) collect one side (either user or product) and broadcast it as a matrix
      2) use level-3 BLAS to compute inner products
      3) use Utils.takeOrdered to find top-k

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              • Assignee:
                debasish83 Debasish Das
                Reporter:
                mengxr Xiangrui Meng
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                • Created:
                  Updated:
                  Resolved: