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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-18781) Allow MatrixFactorizationModel.predict to skip user/product approximation count
Date Sat, 28 Jan 2017 10:21:24 GMT

     [ https://issues.apache.org/jira/browse/SPARK-18781?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sean Owen resolved SPARK-18781.
-------------------------------
    Resolution: Won't Fix

> Allow MatrixFactorizationModel.predict to skip user/product approximation count
> -------------------------------------------------------------------------------
>
>                 Key: SPARK-18781
>                 URL: https://issues.apache.org/jira/browse/SPARK-18781
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Eyal Allweil
>            Priority: Minor
>
> When [MatrixFactorizationModel.predict|https://spark.apache.org/docs/1.6.1/api/java/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.html#predict(org.apache.spark.rdd.RDD)]
is used, it first calculates an approximation count of the users and products in order to
determine the most efficient way to proceed. In many cases, the answer to this question is
fixed (typically there are more users than products by an order of magnitude) and this check
is unnecessary. Adding a parameter to this predict method to allow choosing the implementation
(and skipping the check) would be nice.
> It would be especially nice in development cycles when you are repeatedly tweaking your
model and which pairs you're predicting for and this approximate count represents a meaningful
portion of the time you wait for results.
> I can provide a pull request with this ability added that preserves the existing behavior.



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