GitHub user mengxr reopened a pull request:
https://github.com/apache/spark/pull/165
[SPARK-1266] persist factors in implicit ALS
In implicit ALS computation, the user or product factor is used twice in
each iteration. Caching can certainly help accelerate the computation. I saw
the running time decreased by ~70% for implicit ALS on the movielens data.
I also made the following changes:
1. Change `YtYb` type from `Broadcast[Option[DoubleMatrix]]` to
`Option[Broadcast[DoubleMatrix]]`, so we don't need to broadcast None in
explicit computation.
2. Mark methods `computeYtY`, `unblockFactors`, `updateBlock`, and
`updateFeatures private`. Users do not need those methods.
3. Materialize the final matrix factors before returning the model. It
allows us to clean up other cached RDDs before returning the model. I do not
have a better solution here, so I use `RDD.count()`.
JIRA: https://spark-project.atlassian.net/browse/SPARK-1266
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/165.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 #165
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commit 63862d63a8ae06e21a52918c0097a7f1c843ee5d
Author: Xiangrui Meng <[email protected]>
Date: 2014-03-17T23:20:24Z
persist factors in implicit ALS
commit d3a88aa026b6a3734e6fc515589e604df81913a6
Author: Xiangrui Meng <[email protected]>
Date: 2014-03-18T07:39:29Z
change implicitPrefs match to if ... else ...
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