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

Class DirMF

java.lang.Object

es.upm.etsisi.cf4j.recommender.Recommender

es.upm.etsisi.cf4j.recommender.matrixFactorization.DirMF

public class DirMF
extends Recommender

Implements Lara-Cabrera, R., González, Á., Ortega, F., & González-Prieto, Á. (2022). Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System. Applied Sciences, 12(3), 1223.

Field Summary

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

datamodel

Constructor Summary

Constructors

Constructor	Description
DirMF(DataModel datamodel, int numFactors, int numIters, double learningRate, double regularization, double[] ratings)	Model constructor
DirMF(DataModel datamodel, int numFactors, int numIters, double learningRate, double regularization, double[] ratings, long seed)	Model constructor
DirMF(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	getLearningRate()	Get the learning rate parameter of the model
int	getNumFactors()	Get the number of factors of the model
int	getNumIters()	Get the number of iterations
double[]	getRatings()	Get the discrete ratings values
double	getRegularization()	Get the regularization parameter of the model
double	predict(int userIndex, int itemIndex)	Computes a rating prediction
double	predictProba(int userIndex, int itemIndex)	Computes a prediction probability
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

Constructor Detail

DirMF

public DirMF(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.
• learningRate: double value with the learning rate hyper-parameter.
• regularization: double value with the regularization hyper-parameter.
• ratings: discrete ratings values.
• seed (optional): random seed for random numbers generation. If missing, random value is used.

Parameters:

datamodel - DataModel instance

params - Model's hyper-parameters values

DirMF

public DirMF(DataModel datamodel,
             int numFactors,
             int numIters,
             double learningRate,
             double regularization,
             double[] ratings)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

learningRate - Learning rate

regularization - Regularization

ratings - Discrete ratings values

DirMF

public DirMF(DataModel datamodel,
             int numFactors,
             int numIters,
             double learningRate,
             double regularization,
             double[] ratings,
             long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

learningRate - Learning rate

regularization - Regularization

ratings - Discrete ratings values

seed - Seed for random numbers generation

Method Detail

getNumFactors

public int getNumFactors()

Get the number of factors of the model

Returns:

Number of factors

getNumIters

public int getNumIters()

Get the number of iterations

Returns:

Number of iterations

getLearningRate

public double getLearningRate()

Get the learning rate parameter of the model

Returns:

Learning rate

getRegularization

public double getRegularization()

Get the regularization parameter of the model

Returns:

Regularization

getRatings

public double[] getRatings()

Get the discrete ratings values

Returns:

Discrete ratings values

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

predictProba

public double predictProba(int userIndex,
                           int itemIndex)

Computes a prediction probability

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 probability

toString

public String toString()

Overrides:

toString in class Object