Package es.upm.etsisi.cf4j.recommender.matrixFactorization

Class SVDPlusPlus

java.lang.Object

es.upm.etsisi.cf4j.recommender.Recommender

es.upm.etsisi.cf4j.recommender.matrixFactorization.SVDPlusPlus

public class SVDPlusPlus
extends Recommender

Implements Koren, Y. (2008, August). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434).

Field Summary

Fields

Modifier and Type	Field	Description
protected double[]	bi	bi parameter
protected double[]	bu	bu parameter
protected static double	DEFAULT_GAMMA
protected static double	DEFAULT_LAMBDA
protected double	gamma	Learning rate hyper-parameter
protected double	lambda	Regularization hyper-parameter
protected int	numFactors	Number of latent factors
protected int	numIters	Number of iterations
protected double[][]	p	p parameter
protected double[][]	q	q parameter
protected double[][]	y	y parameter

Fields inherited from class es.upm.etsisi.cf4j.recommender.Recommender

datamodel

Constructor Summary

Constructors

Constructor	Description
SVDPlusPlus(DataModel datamodel, int numFactors, int numIters)	Model constructor
SVDPlusPlus(DataModel datamodel, int numFactors, int numIters, double gamma, double lambda)	Model constructor
SVDPlusPlus(DataModel datamodel, int numFactors, int numIters, double gamma, double lambda, long seed)	Model constructor
SVDPlusPlus(DataModel datamodel, int numFactors, int numIters, long seed)	Model constructor
SVDPlusPlus(DataModel datamodel, Map<String,Object> params)	Model constructor from a Map containing the model's hyper-parameters values.

Method Summary

Modifier and Type	Method	Description
void	fit()	Estimates model parameters given the hyper-parameters
double	getGamma()	Getter of the gamma value.
double	getLambda()	Getter of the Lambda value.
int	getNumFactors()	Number of factors used in this recommender.
int	getNumIters()	Number of iterations used in this recommender.
double	predict(int userIndex, int itemIndex)	Computes a rating prediction
String	toString()

Methods inherited from class es.upm.etsisi.cf4j.recommender.Recommender

getDataModel, predict

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

DEFAULT_GAMMA

protected static final double DEFAULT_GAMMA

See Also:

Constant Field Values

DEFAULT_LAMBDA

protected static final double DEFAULT_LAMBDA

See Also:

Constant Field Values

numFactors

protected final int numFactors

Number of latent factors

numIters

protected final int numIters

Number of iterations

gamma

protected final double gamma

Learning rate hyper-parameter

lambda

protected final double lambda

Regularization hyper-parameter

bu

protected final double[] bu

bu parameter

bi

protected final double[] bi

bi parameter

p

protected final double[][] p

p parameter

q

protected final double[][] q

q parameter

y

protected final double[][] y

y parameter

Constructor Detail

SVDPlusPlus

public SVDPlusPlus(DataModel datamodel,
                   Map<String,Object> params)

Model constructor from a Map containing the model's hyper-parameters values. Map object must contains the following keys:

• numFactors: int value with the number of latent factors.
• numIters:: int value with the number of iterations.
• gamma (optional): double value with the learning rate hyper-parameter. If missing, it is set to 0.01.
• lambda (optional): double value with the regularization hyper-parameter. If missing, it is set to 0.05.
• seed (optional): random seed for random numbers generation. If missing, random value is used.

Parameters:

datamodel - DataModel instance

params - Model's hyper-parameters values

SVDPlusPlus

public SVDPlusPlus(DataModel datamodel,
                   int numFactors,
                   int numIters)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

SVDPlusPlus

public SVDPlusPlus(DataModel datamodel,
                   int numFactors,
                   int numIters,
                   long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

seed - Seed for random numbers generation

SVDPlusPlus

public SVDPlusPlus(DataModel datamodel,
                   int numFactors,
                   int numIters,
                   double gamma,
                   double lambda)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

gamma - Learning rate hyper-parameter

lambda - Regularization hyper-parameter

SVDPlusPlus

public SVDPlusPlus(DataModel datamodel,
                   int numFactors,
                   int numIters,
                   double gamma,
                   double lambda,
                   long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

gamma - Learning rate hyper-parameter

lambda - Regularization hyper-parameter

seed - Seed for random numbers generation

Method Detail

fit

public void fit()

Description copied from class: Recommender

Estimates model parameters given the hyper-parameters

Specified by:

fit in class Recommender

predict

public double predict(int userIndex,
                      int itemIndex)

Description copied from class: Recommender

Computes a rating prediction

Specified by:

predict in class Recommender

Parameters:

userIndex - Index of the user in the array of Users of the DataModel instance

itemIndex - Index of the item in the array of Items of the DataModel instance

Returns:

Prediction

toString

public String toString()

Overrides:

toString in class Object

getNumFactors

public int getNumFactors()

Number of factors used in this recommender.

Returns:

Number of factors

getNumIters

public int getNumIters()

Number of iterations used in this recommender.

Returns:

Number of factors

getGamma

public double getGamma()

Getter of the gamma value.

Returns:

Gamma value

getLambda

public double getLambda()

Getter of the Lambda value.

Returns:

Lambda value