org.apache.mahout.cf.taste.impl.recommender.svd
Class ALSWRFactorizer

java.lang.Object
  extended by org.apache.mahout.cf.taste.impl.recommender.svd.AbstractFactorizer
      extended by org.apache.mahout.cf.taste.impl.recommender.svd.ALSWRFactorizer
All Implemented Interfaces:
Refreshable, Factorizer

public class ALSWRFactorizer
extends AbstractFactorizer

factorizes the rating matrix using "Alternating-Least-Squares with Weighted-λ-Regularization" as described in the paper "Large-scale Collaborative Filtering for the Netflix Prize"


Constructor Summary
ALSWRFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations)
           
 
Method Summary
protected  ExecutorService createQueue()
           
 Factorization factorize()
           
protected  Vector ratingVector(PreferenceArray prefs)
           
 
Methods inherited from class org.apache.mahout.cf.taste.impl.recommender.svd.AbstractFactorizer
createFactorization, itemIndex, refresh, userIndex
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

ALSWRFactorizer

public ALSWRFactorizer(DataModel dataModel,
                       int numFeatures,
                       double lambda,
                       int numIterations)
                throws TasteException
Throws:
TasteException
Method Detail

factorize

public Factorization factorize()
                        throws TasteException
Throws:
TasteException

createQueue

protected ExecutorService createQueue()

ratingVector

protected Vector ratingVector(PreferenceArray prefs)


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