prea.recommender.etc
Class SlopeOne

java.lang.Object
  prea.recommender.etc.SlopeOne

All Implemented Interfaces:

Recommender

public class SlopeOne
extends java.lang.Object
implements Recommender

This is a class implementing Slope-One algorithm. Technical detail of the algorithm can be found in Daniel Lemire and Anna Maclachlan, Slope One Predictors for Online Rating-Based Collaborative Filtering, Society for Industrial Mathematics, 05:471-480, 2005.

Since:

2012. 4. 20

Version:

1.1

Author:

Joonseok Lee

Field Summary

private DenseMatrix	diffMatrix Prepared difference matrix
private DenseMatrix	freqMatrix Prepared frequency matrix
int	itemCount The number of items.
double	maxValue Maximum value of rating, existing in the dataset.
double	minValue Minimum value of rating, existing in the dataset.
SparseMatrix	rateMatrix Rating matrix for each user (row) and item (column)
int	userCount The number of users.

Constructor Summary

SlopeOne(int uc, int ic, double max, double min)
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.

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)

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.

diffMatrix

private DenseMatrix diffMatrix

Prepared difference matrix

freqMatrix

private DenseMatrix freqMatrix

Prepared frequency matrix

Constructor Detail

SlopeOne

public SlopeOne(int uc,
                int ic,
                double max,
                double min)

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.

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.