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https://issues.apache.org/jira/browse/SPARK-2085?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-2085.
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Resolution: Implemented
Fix Version/s: 1.1.0
Target Version/s: 1.1.0
PR: https://github.com/apache/spark/pull/1026
> Apply user-specific regularization instead of uniform regularization in
> Alternating Least Squares (ALS)
> -------------------------------------------------------------------------------------------------------
>
> Key: SPARK-2085
> URL: https://issues.apache.org/jira/browse/SPARK-2085
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.0.0
> Reporter: Shuo Xiang
> Priority: Minor
> Fix For: 1.1.0
>
>
> The current implementation of ALS takes a single regularization parameter and
> apply it on both of the user factors and the product factors. This kind of
> regularization can be less effective while user number is significantly
> larger than the number of products (and vice versa). For example, if we have
> 10M users and 1K product, regularization on user factors will dominate.
> Following the discussion in [this
> thread](http://apache-spark-user-list.1001560.n3.nabble.com/possible-bug-in-Spark-s-ALS-implementation-tt2567.html#a2704),
> the implementation in this PR will regularize each factor vector by #ratings
> * lambda.
> Link to PR: https://github.com/apache/spark/pull/1026
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