Package es.upm.etsisi.cf4j.recommender.neural

Class MLP

java.lang.Object

es.upm.etsisi.cf4j.recommender.Recommender

es.upm.etsisi.cf4j.recommender.neural.MLP

public class MLP
extends Recommender

Implements He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182).

Field Summary

Fields

Modifier and Type	Field	Description
protected int[]	layers	Array of layers neurons
protected double	learningRate	Learning rate
protected int	numFactors	Number of factors

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

datamodel

Constructor Summary

Constructors

Constructor	Description
MLP(DataModel datamodel, int numEpochs, double learningRate)	Model constructor
MLP(DataModel datamodel, int numEpochs, double learningRate, int[] layers)	Model constructor
MLP(DataModel datamodel, int numEpochs, double learningRate, long seed)	Model constructor
MLP(DataModel datamodel, int numFactors, int numEpochs, double learningRate)	Model constructor
MLP(DataModel datamodel, int numFactors, int numEpochs, double learningRate, int[] layers)	Model constructor
MLP(DataModel datamodel, int numFactors, int numEpochs, double learningRate, int[] layers, long seed)	Model constructor
MLP(DataModel datamodel, int numFactors, int numEpochs, double learningRate, long seed)	Model constructor
MLP(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
int[]	getLayers()	Returns net layers.
double	getLearningRate()	Returns learning rate.
int	getNumEpochs()	Returns the number of epochs.
int	getNumFactors()	Returns the number of latent factors.
double	predict(int userIndex, int itemIndex)	Returns the prediction of a rating of a certain user for a certain item, through these predictions the metrics of MAE, MSE and RMSE can be obtained.
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

learningRate

protected final double learningRate

Learning rate

numFactors

protected final int numFactors

Number of factors

layers

protected final int[] layers

Array of layers neurons

Constructor Detail

MLP

public MLP(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 (optional): int value with the number of factors. If missing, default 10 latent factors are used.
• numEpochs: int value with the number of epochs.
• learningRate: double value with the learning rate.
• layers (optional): Array of layers neurons. If missing, default [20, 10] Array is used.
• seed (optional): random seed for random numbers generation. If missing, random value is used.

Parameters:

datamodel - DataModel instance

params - Model's hyper-parameters values

MLP

public MLP(DataModel datamodel,
           int numFactors,
           int numEpochs,
           double learningRate)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of factors. 10 by default

numEpochs - Number of epochs

learningRate - Learning rate

MLP

public MLP(DataModel datamodel,
           int numFactors,
           int numEpochs,
           double learningRate,
           long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of factors. 10 by default

numEpochs - Number of epochs

learningRate - Learning rate

seed - Seed for random numbers generation

MLP

public MLP(DataModel datamodel,
           int numFactors,
           int numEpochs,
           double learningRate,
           int[] layers)

Model constructor

Parameters:

datamodel - DataModel instance

numEpochs - Number of epochs. 10 by default

learningRate - Learning rate

numFactors - Number of factors

layers - Array of layers neurons. [20, 10] by default

MLP

public MLP(DataModel datamodel,
           int numEpochs,
           double learningRate)

Model constructor

Parameters:

datamodel - DataModel instance

numEpochs - Number of epochs

learningRate - Learning rate

MLP

public MLP(DataModel datamodel,
           int numEpochs,
           double learningRate,
           long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numEpochs - Number of epochs

learningRate - Learning rate

seed - Seed for random numbers generation

MLP

public MLP(DataModel datamodel,
           int numEpochs,
           double learningRate,
           int[] layers)

Model constructor

Parameters:

datamodel - DataModel instance

learningRate - Learning rate

layers - Array of layers neurons. [20, 10] by default

MLP

public MLP(DataModel datamodel,
           int numFactors,
           int numEpochs,
           double learningRate,
           int[] layers,
           long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numEpochs - Number of epochs

learningRate - Learning rate

numFactors - Number of factors

layers - Array of layers neurons. [20, 10] by default.

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)

Returns the prediction of a rating of a certain user for a certain item, through these predictions the metrics of MAE, MSE and RMSE can be obtained.

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

getNumEpochs

public int getNumEpochs()

Returns the number of epochs.

Returns:

Number of epochs.

getNumFactors

public int getNumFactors()

Returns the number of latent factors.

Returns:

Number of latent factors.

getLearningRate

public double getLearningRate()

Returns learning rate.

Returns:

learning rate.

getLayers

public int[] getLayers()

Returns net layers.

Returns:

net layers.

toString

public String toString()

Overrides:

toString in class Object