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SparkALR

Alternating logistic regression is a collaborative filtering method for the prediction of occurance probability given binary observations (e.g. click-though rate).

Compilation

To compile:

sbt/sbt assembly

Run

To run with 4GB of ram:

./bin/spark-submit --class org.apache.spark.ml.examples.SparkALR \
    ./examples/target/scala-2.10/spark-examples-1.6.2-SNAPSHOT-hadoop2.2.0.jar \
    --executor-memory 4G  --driver-memory 4G

Implementations

Detailed information can be found here.

All the implementations are in the SparkALR.scala except the localTrain method for LogisticRegression() which is in LogisiticRegression.scala.

Sample data is included at data/mllib/SparkALR.data.csv