prea.recommender.memory
Class MemoryBasedRecommender

java.lang.Object
  prea.recommender.memory.MemoryBasedRecommender

All Implemented Interfaces:

Recommender

Direct Known Subclasses:

ItemBased, UserBased

public abstract class MemoryBasedRecommender
extends java.lang.Object
implements Recommender

The class implementing two memory-based (neighborhood-based) methods, predicting by referring to rating matrix for each query. Contains user-based and item-based, with some variations on them.

Since:

2012. 4. 20

Version:

1.1

Author:

Joonseok Lee

Field Summary

double	defaultValue The default voting value, if used.
boolean	defaultVote Indicating whether to use default vote value.
static int	INVERSE_USER_FREQUENCY Similarity Measure Code for Inverse User Frequency
int	itemCount The number of items.
int	maxValue Maximum value of rating, existing in the dataset.
static int	MEAN_ABS_DIFF Similarity Measure Code for Mean Absolute Difference (MAD)
static int	MEAN_SQUARE_DIFF Similarity Measure Code for Mean Squared Difference (MSD)
int	minValue Minimum value of rating, existing in the dataset.
int	neighborSize The number of neighbors, used for estimation.
static int	PEARSON_CORR Similarity Measure Code for Pearson Correlation
SparseMatrix	rateMatrix Rating matrix for each user (row) and item (column)
int	similarityMethod The method code for similarity measure.
static int	SIMPLE_WEIGHTED_AVG Estimation Method Code for Simple Weighted Average
int	userCount The number of users.
static int	VECTOR_COS Similarity Measure Code for Vector Cosine
static int	WEIGHTED_SUM Estimation Method Code for Weighted Sum

Constructor Summary

MemoryBasedRecommender(int uc, int ic, int max, int min, int ns, int sim, boolean df, double dv)
Construct a memory-based model with the given data.

Method Summary

void	buildModel(SparseMatrix rm) Build a model with given training set.
double	similarity(boolean rowOriented, SparseVector i1, SparseVector i2, double i1Avg, double i2Avg, int method) Calculate similarity between two given vectors.

Methods inherited from class java.lang.Object

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

Methods inherited from interface prea.recommender.Recommender

evaluate

Field Detail

PEARSON_CORR

public static final int PEARSON_CORR

Similarity Measure Code for Pearson Correlation

See Also:

Constant Field Values

VECTOR_COS

public static final int VECTOR_COS

Similarity Measure Code for Vector Cosine

See Also:

Constant Field Values

MEAN_SQUARE_DIFF

public static final int MEAN_SQUARE_DIFF

Similarity Measure Code for Mean Squared Difference (MSD)

See Also:

Constant Field Values

MEAN_ABS_DIFF

public static final int MEAN_ABS_DIFF

Similarity Measure Code for Mean Absolute Difference (MAD)

See Also:

Constant Field Values

INVERSE_USER_FREQUENCY

public static final int INVERSE_USER_FREQUENCY

Similarity Measure Code for Inverse User Frequency

See Also:

Constant Field Values

WEIGHTED_SUM

public static final int WEIGHTED_SUM

Estimation Method Code for Weighted Sum

See Also:

Constant Field Values

SIMPLE_WEIGHTED_AVG

public static final int SIMPLE_WEIGHTED_AVG

Estimation Method Code for Simple Weighted Average

See Also:

Constant Field Values

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 int maxValue

Maximum value of rating, existing in the dataset.

minValue

public int minValue

Minimum value of rating, existing in the dataset.

neighborSize

public int neighborSize

The number of neighbors, used for estimation.

similarityMethod

public int similarityMethod

The method code for similarity measure.

defaultVote

public boolean defaultVote

Indicating whether to use default vote value.

defaultValue

public double defaultValue

The default voting value, if used.

Constructor Detail

MemoryBasedRecommender

public MemoryBasedRecommender(int uc,
                              int ic,
                              int max,
                              int min,
                              int ns,
                              int sim,
                              boolean df,
                              double dv)

Construct a memory-based 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.

ns - The neighborhood size.

sim - The method code of similarity measure.

df - Indicator whether to use default values.

dv - Default value if used.

Method Detail

buildModel

public void buildModel(SparseMatrix rm)

Build a model with given training set.

Specified by:

buildModel in interface Recommender

Parameters:

rm - Training data set.

similarity

public double similarity(boolean rowOriented,
                         SparseVector i1,
                         SparseVector i2,
                         double i1Avg,
                         double i2Avg,
                         int method)

Calculate similarity between two given vectors.

Parameters:

rowOriented - Use true if user-based, false if item-based.

i1 - The first vector to calculate similarity.

i2 - The second vector to calculate similarity.

i1Avg - The average of elements in the first vector.

i2Avg - The average of elements in the second vector.

method - The code of similarity measure to be used. It can be one of the following: PEARSON_CORR, VECTOR_COS, MEAN_SQUARE_DIFF, MEAN_ABS_DIFF, or INVERSE_USER_FREQUENCY.

Returns:

The similarity value between two vectors i1 and i2.