streaming-matrix-factorization (homepage)
Streaming Recommendation Engine using matrix factorization with user and product bias
@brkyvz / (2)
Matrix Factorization Model for Recommendation Systems. The model consists of
- user factors (User Matrix, `U`),
- product factors (Product Matrix, `P^T^`),
- user biases (user bias vector, `bu`),
- product biases (product bias vector, `bp`) and
- the global bias (global average, `mu`).
A rating is predicted as:
r = U(u) * P^T^(i) + bu(u) + bp(i) + mu
Can be trained both on a stream or a single RDD and predictions can be made to a stream
or a single RDD.
Tags
How to
Include this package in your Spark Applications using:
spark-shell, pyspark, or spark-submit
> $SPARK_HOME/bin/spark-shell --packages brkyvz:streaming-matrix-factorization:0.1.0
sbt
If you use the sbt-spark-package plugin, in your sbt build file, add:
spDependencies += "brkyvz/streaming-matrix-factorization:0.1.0"
Otherwise,
resolvers += "Spark Packages Repo" at "https://repos.spark-packages.org/" libraryDependencies += "brkyvz" % "streaming-matrix-factorization" % "0.1.0"
Maven
In your pom.xml, add:<dependencies>
<!-- list of dependencies -->
<dependency>
<groupId>brkyvz</groupId>
<artifactId>streaming-matrix-factorization</artifactId>
<version>0.1.0</version>
</dependency>
</dependencies>
<repositories>
<!-- list of other repositories -->
<repository>
<id>SparkPackagesRepo</id>
<url>https://repos.spark-packages.org/</url>
</repository>
</repositories>
Releases
Version: 0.1.0 ( edfdbf | zip | jar ) / Date: 2015-05-26 / License: Apache-2.0 / Scala version: 2.10