prea.recommender.matrix
Class NMF

java.lang.Object
  prea.recommender.matrix.MatrixFactorizationRecommender
      prea.recommender.matrix.NMF

All Implemented Interfaces:

Recommender

public class NMF
extends MatrixFactorizationRecommender

This is a class implementing Non-negative Matrix Factorization. Technical detail of the algorithm can be found in Daniel D. Lee and H. Sebastian Seung, Algorithms for Non-negative Matrix Factorization, Advances in Neural Information Processing Systems, 2001.

Since:

2012. 4. 20

Version:

1.1

Author:

Joonseok Lee

Field Summary

private SparseMatrix	validationMatrix Rating matrix for items which will be used during the validation phase.
private double	validationRatio Proportion of dataset, using for validation purpose.

Fields inherited from class prea.recommender.matrix.MatrixFactorizationRecommender

featureCount, itemCount, itemFeatures, learningRate, maxIter, maxValue, minValue, momentum, offset, regularizer, showProgress, userCount, userFeatures

Constructor Summary

NMF(int uc, int ic, double max, double min, int fc, double lr, double r, double m, int iter, double vr, boolean verbose)
Construct a matrix-factorization model with the given data.

Method Summary

void	buildModel(SparseMatrix rateMatrix) Build a model with given training set.
private void	makeValidationSet(SparseMatrix rateMatrix, double validationRatio) Items which will be used for validation purpose are moved from rateMatrix to validationMatrix.
private void	restoreValidationSet(SparseMatrix rateMatrix) Items in validationMatrix are moved to original rateMatrix.

Methods inherited from class prea.recommender.matrix.MatrixFactorizationRecommender

evaluate

Methods inherited from class java.lang.Object

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

Field Detail

validationMatrix

private SparseMatrix validationMatrix

Rating matrix for items which will be used during the validation phase. Not allowed to refer during training phase.

validationRatio

private double validationRatio

Proportion of dataset, using for validation purpose.

Constructor Detail

NMF

public NMF(int uc,
           int ic,
           double max,
           double min,
           int fc,
           double lr,
           double r,
           double m,
           int iter,
           double vr,
           boolean verbose)

Construct a matrix-factorization model with the given data.

Parameters:

uc - The number of users in the dataset.

ic - The number of items in the dataset.

max - The maximum rating value in the dataset.

min - The minimum rating value in the dataset.

fc - The number of features in low-rank factorized matrix.

lr - The learning rate for gradient-descent method.

r - The regularization factor.

m - The momentum parameter.

iter - The maximum number of iteration.

verbose - Show progress of iterative methods.

Method Detail

buildModel

public void buildModel(SparseMatrix rateMatrix)

Build a model with given training set.

Specified by:

buildModel in interface Recommender

Overrides:

buildModel in class MatrixFactorizationRecommender

Parameters:

rateMatrix - Training data set.

makeValidationSet

private void makeValidationSet(SparseMatrix rateMatrix,
                               double validationRatio)

Items which will be used for validation purpose are moved from rateMatrix to validationMatrix.

Parameters:

validationRatio - Proportion of dataset, using for validation purpose.

restoreValidationSet

private void restoreValidationSet(SparseMatrix rateMatrix)

Items in validationMatrix are moved to original rateMatrix.