prea.recommender.etc
Class FastNPCA

java.lang.Object
  prea.recommender.etc.FastNPCA

All Implemented Interfaces:

Recommender

public class FastNPCA
extends java.lang.Object
implements Recommender

This is a class implementing Fast Nonparametric Principal Component Analysis (NPCA). Technical detail of the algorithm can be found in Kai Yu et al, Fast Nonparametric Matrix Factorization for Large-scale Collaborative Filtering, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009.

Since:

2012. 4. 20

Version:

1.1

Author:

Joonseok Lee

Field Summary

int	itemCount The number of items.
private SparseMatrix	K
int	maxIter Maximum number of iteration.
double	maxValue Maximum value of rating, existing in the dataset.
double	minValue Minimum value of rating, existing in the dataset.
private SparseVector	mu
SparseMatrix	rateMatrix Rating matrix for each user (row) and item (column)
boolean	showProgress Indicator whether to show progress of iteration.
int	userCount The number of users.
private SparseMatrix	validationMatrix Rating matrix for items which will be used during the validation phase.
double	validationRatio Proportion of dataset, using for validation purpose.

Constructor Summary

FastNPCA(int uc, int ic, double max, double min, double vr, int iter)
Construct a Fast NPCA model with the given data.

Method Summary

void	buildModel(SparseMatrix rm) Build a model with the given data and algorithm.
EvaluationMetrics	evaluate(SparseMatrix testMatrix) Evaluate the designated algorithm with the given test data.
private SparseVector	getEstimation(int u, int[] testItems) Estimate of ratings for a given user and a set of test items.
private void	makeValidationSet(double validationRatio) Items which will be used for validation purpose are moved from rateMatrix to validationMatrix.
private void	restoreValidationSet() Items in validationMatrix are moved to original rateMatrix.

Methods inherited from class java.lang.Object

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

Field Detail

rateMatrix

public SparseMatrix rateMatrix

Rating matrix for each user (row) and item (column)

validationMatrix

private SparseMatrix validationMatrix

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

userCount

public int userCount

The number of users.

itemCount

public int itemCount

The number of items.

maxValue

public double maxValue

Maximum value of rating, existing in the dataset.

minValue

public double minValue

Minimum value of rating, existing in the dataset.

maxIter

public int maxIter

Maximum number of iteration.

validationRatio

public double validationRatio

Proportion of dataset, using for validation purpose.

showProgress

public boolean showProgress

Indicator whether to show progress of iteration.

K

private SparseMatrix K

mu

private SparseVector mu

Constructor Detail

FastNPCA

public FastNPCA(int uc,
                int ic,
                double max,
                double min,
                double vr,
                int iter)

Construct a Fast NPCA 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.

vr - The proportion of dataset which will be used for validation.

iter - The maximum number of iteration.

Method Detail

buildModel

public void buildModel(SparseMatrix rm)

Build a model with the given data and algorithm.

Specified by:

buildModel in interface Recommender

Parameters:

rm - The rating matrix with train data.

evaluate

public EvaluationMetrics evaluate(SparseMatrix testMatrix)

Evaluate the designated algorithm with the given test data.

Specified by:

evaluate in interface Recommender

Parameters:

testMatrix - The rating matrix with test data.

Returns:

The result of evaluation, such as MAE, RMSE, and rank-score.

getEstimation

private SparseVector getEstimation(int u,
                                   int[] testItems)

Estimate of ratings for a given user and a set of test items.

Parameters:

u - The user number.

testItems - The list of items to be predicted.

Returns:

A list containing predicted rating scores.

makeValidationSet

private void makeValidationSet(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()

Items in validationMatrix are moved to original rateMatrix.