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| author | Shuo Xiang <sxiang@twitter.com> | 2014-06-12 17:37:06 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2014-06-12 17:37:06 -0700 |
| commit | a6e0afdcf0174425e8a6ff20b2bc2e3a7a374f19 (patch) | |
| tree | 380e0147d1a617480bfa9efa14753c88f99ccc32 /core | |
| parent | 1c04652c8f18566baafb13dbae355f8ad2ad8d37 (diff) | |
| download | spark-a6e0afdcf0174425e8a6ff20b2bc2e3a7a374f19.tar.gz spark-a6e0afdcf0174425e8a6ff20b2bc2e3a7a374f19.tar.bz2 spark-a6e0afdcf0174425e8a6ff20b2bc2e3a7a374f19.zip | |
SPARK-2085: [MLlib] Apply user-specific regularization instead of uniform regularization in ALS
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.
Author: Shuo Xiang <sxiang@twitter.com>
Closes #1026 from coderxiang/als-reg and squashes the following commits:
93dfdb4 [Shuo Xiang] Merge remote-tracking branch 'upstream/master' into als-reg
b98f19c [Shuo Xiang] merge latest master
52c7b58 [Shuo Xiang] Apply user-specific regularization instead of uniform regularization in Alternating Least Squares (ALS)
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