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

Class ParallelSGDFactorizer

java.lang.Object

org.apache.mahout.cf.taste.impl.recommender.svd.AbstractFactorizer

org.apache.mahout.cf.taste.impl.recommender.svd.ParallelSGDFactorizer

All Implemented Interfaces:

Refreshable, Factorizer

public class ParallelSGDFactorizer
extends AbstractFactorizer

Minimalistic implementation of Parallel SGD factorizer based on "Scalable Collaborative Filtering Approaches for Large Recommender Systems" and "Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent"

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static class	ParallelSGDFactorizer.PreferenceShuffler

Field Summary

Fields

Modifier and Type	Field and Description
protected double[][]	itemVectors item features
protected double[][]	userVectors user features

Constructor Summary

Constructors

Constructor and Description
ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numEpochs)
ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, double mu0, double decayFactor, int stepOffset, double forgettingExponent)
ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, double mu0, double decayFactor, int stepOffset, double forgettingExponent, double biasMuRatio, double biasLambdaRatio)
ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, double mu0, double decayFactor, int stepOffset, double forgettingExponent, double biasMuRatio, double biasLambdaRatio, int numThreads)
ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, double mu0, double decayFactor, int stepOffset, double forgettingExponent, int numThreads)

Method Summary

Modifier and Type	Method and Description
Factorization	factorize()
protected void	initialize()
protected void	update(Preference preference, double mu) TODO: this is the vanilla sgd by Tacaks 2009, I speculate that using scaling technique proposed in: Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent section 5, page 6 can be beneficial in term s of both speed and accuracy.

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

Field Detail

userVectors

protected volatile double[][] userVectors

user features

itemVectors

protected volatile double[][] itemVectors

item features

Constructor Detail

ParallelSGDFactorizer

public ParallelSGDFactorizer(DataModel dataModel,
                             int numFeatures,
                             double lambda,
                             int numEpochs)
                      throws TasteException

Throws:

TasteException

ParallelSGDFactorizer

public ParallelSGDFactorizer(DataModel dataModel,
                             int numFeatures,
                             double lambda,
                             int numIterations,
                             double mu0,
                             double decayFactor,
                             int stepOffset,
                             double forgettingExponent)
                      throws TasteException

Throws:

TasteException

ParallelSGDFactorizer

public ParallelSGDFactorizer(DataModel dataModel,
                             int numFeatures,
                             double lambda,
                             int numIterations,
                             double mu0,
                             double decayFactor,
                             int stepOffset,
                             double forgettingExponent,
                             int numThreads)
                      throws TasteException

Throws:

TasteException

ParallelSGDFactorizer

public ParallelSGDFactorizer(DataModel dataModel,
                             int numFeatures,
                             double lambda,
                             int numIterations,
                             double mu0,
                             double decayFactor,
                             int stepOffset,
                             double forgettingExponent,
                             double biasMuRatio,
                             double biasLambdaRatio)
                      throws TasteException

Throws:

TasteException

ParallelSGDFactorizer

public ParallelSGDFactorizer(DataModel dataModel,
                             int numFeatures,
                             double lambda,
                             int numIterations,
                             double mu0,
                             double decayFactor,
                             int stepOffset,
                             double forgettingExponent,
                             double biasMuRatio,
                             double biasLambdaRatio,
                             int numThreads)
                      throws TasteException

Throws:

TasteException

Method Detail

initialize

protected void initialize()
                   throws TasteException

Throws:

TasteException

factorize

public Factorization factorize()
                        throws TasteException

Throws:

TasteException

update

protected void update(Preference preference,
                      double mu)

TODO: this is the vanilla sgd by Tacaks 2009, I speculate that using scaling technique proposed in: Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent section 5, page 6 can be beneficial in term s of both speed and accuracy. Tacaks' method doesn't calculate gradient of regularization correctly, which has non-zero elements everywhere of the matrix. While Tacaks' method can only updates a single row/column, if one user has a lot of recommendation, her vector will be more affected by regularization using an isolated scaling factor for both user vectors and item vectors can remove this issue without inducing more update cost it even reduces it a bit by only performing one addition and one multiplication. BAD SIDE1: the scaling factor decreases fast, it has to be scaled up from time to time before dropped to zero or caused roundoff error BAD SIDE2: no body experiment on it before, and people generally use very small lambda so it's impact on accuracy may still be unknown. BAD SIDE3: don't know how to make it work for L1-regularization or "pseudorank?" (sum of singular values)-regularization