Streaming Matrix Factorization for Spark

This library contains methods to train a Matrix Factorization Recommendation System on Spark. For user u and item i, the rating is calculated as:

r = U(u) * P^T^(i) + bu(u) + bp(i) + mu,

where r is the rating, U is the User Matrix, P^T^ is the transpose of the product matrix, U(u) corresponds to the uth row of U, bu(u) is the bias of the uth user, bp(i) is the bias of the ith product and mu is the average global rating.

Gradient Descent is used to train the model.

Installation

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 "http://dl.bintray.com/spark-packages/maven"
		  
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>http://dl.bintray.com/spark-packages/maven</url>
  </repository>
</repositories>

Usage

To train a streaming model, use the StreamingLatentMatrixFactorization class. The following usage will train a Model that would predict ratings between 1.0, and 5.0 with rank 20:

import com.brkyvz.spark.recommendation.StreamingLatentMatrixFactorization
import org.apache.spark.ml.recommendation.ALS.Rating
import org.apache.spark.streaming.dstream.DStream

val ratingStream: DStream[Rating[Long]] = ... // Your input stream of Ratings
// numUsers and numProducts are the number of users and products respectively
val algorithm = new StreamingLatentMatrixFactorization(numUsers, numProducts)
algorithm.trainOn(ratingStream)

val testStream: DStream[(Long, Long)] = ... // stream of (user, product) pairs to predict on
val predictions: DStream[Rating[Long]] = algorithm.predictOn(testStream)

You can also predict on a static RDD

val latestModel = algorithm.latestModel()
val testData: RDD[(Long, Long)] = ... // RDD of (user, product) pairs to predict on
val predictions: RDD[Rating[Long]] = latestModel.predict(testData)

You can also train on a static RDD and then predict on a DStream or RDD

import com.brkyvz.spark.recommendation.StreamingLatentMatrixFactorization
import org.apache.spark.ml.recommendation.ALS.Rating
import org.apache.spark.streaming.dstream.DStream

val ratings: RDD[Rating[Long]] = ... // Your input stream of Ratings
// numUsers and numProducts are the number of users and products respectively
val algorithm = new LatentMatrixFactorization(numUsers, numProducts)
algorithm.trainOn(ratings)

val testStream: DStream[(Long, Long)] = ... // stream of (user, product) pairs to predict on
val predictions: DStream[Rating[Long]] = algorithm.predictOn(testStream)