GitHub user mengxr opened a pull request:
https://github.com/apache/spark/pull/131
[SPARK-1237, 1238] Improve the computation of YtY for implicit ALS
Computing YtY can be implemented using BLAS's DSPR operations instead of
generating y_i y_i^T and then combining them. The latter generates many k-by-k
matrices. On the movielens data, this change improves the performance by
10-20%. The algorithm remains the same, verified by computing RMSE on the
movielens data.
To compare the results, I also added an option to set a random seed in ALS.
JIRA:
1. https://spark-project.atlassian.net/browse/SPARK-1237
2. https://spark-project.atlassian.net/browse/SPARK-1238
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/mengxr/spark als
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/131.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #131
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commit 200bef0c18bc1309c83366be94009eadd460befc
Author: Xiangrui Meng <m...@databricks.com>
Date: 2014-03-12T21:06:05Z
optimize computeYtY and updateBlock
commit 4c7cde24b7973f881431642c21e8820aa540b340
Author: Xiangrui Meng <m...@databricks.com>
Date: 2014-03-13T00:30:12Z
allow specifying a random seed in ALS
commit 2fc164163656eeffba4b2bd3dc746b117c15de22
Author: Xiangrui Meng <m...@databricks.com>
Date: 2014-03-13T00:32:24Z
remove commented code
commit d984623d1702a89cf948e55ce84153eb59c97ebd
Author: Xiangrui Meng <m...@databricks.com>
Date: 2014-03-13T00:50:35Z
minor changes
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