GitHub user zhengruifeng opened a pull request:
https://github.com/apache/spark/pull/5591
[SPARK-7008] An implement of Factorization Machines based on Scala and
Spark MLlib.
An implementation of Factorization Machines based on Scala and Spark MLlib.
Factorization Machine is a kind of state-of-the-art machine learning
algorithm for multi-linear regression, and is widely used in recommendation
systems.
Factorization Machines algorithm and its C++ implement LibFM works well in
recent years' recommendation competitions.
A FMModel consist of three parts:
an Intercept (optional),
an one-way interactions weights like other linear models (optional),
a numFactors * numFeatures matrix representing the factors of each
feature (mandatory).
I implement the training algorithm with SGD provided by MLlib's
GradientDescent (to use MLlib's SGD, the model is encoded to a dense vector
during training).
Ref:
http://libfm.org/
http://doi.acm.org/10.1145/2168752.2168771
http://www.inf.uni-konstanz.de/~rendle/pdf/Rendle2010FM.pdf
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/zhengruifeng/spark-factorization-machine
master
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/5591.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 #5591
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commit 18c3a494c4ccca1843748023f21cda34c5ecd9a8
Author: zhengruifeng <ruife...@foxmail.com>
Date: 2015-04-20T01:43:42Z
add FactorizationMachine.scala
commit 2b49d74805e02a21cf0e5a0aa022493f19868a4b
Author: zhengruifeng <ruife...@foxmail.com>
Date: 2015-04-20T09:13:04Z
Add Test
commit dfd0c3b54ec016b8cbc233d44432c9e38ed9570e
Author: zhengruifeng <ruife...@foxmail.com>
Date: 2015-04-20T10:46:35Z
Add comments
commit ba33af8fa67c568b5130392e7c3a38456405cfc0
Author: zhengruifeng <ruife...@foxmail.com>
Date: 2015-04-20T10:53:42Z
some comments
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