prea.recommender.baseline
Class BaselineRecommender

java.lang.Object
  prea.recommender.baseline.BaselineRecommender

All Implemented Interfaces:

Recommender

Direct Known Subclasses:

Average, Constant, ItemAverage, Random, UserAverage

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

This is an abstract class implementing five baselines, including constant model, overall average, user average, item average, and random.

Since:

2012. 4. 20

Version:

1.1

Author:

Joonseok Lee

Field Summary

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.
protected SparseMatrix	rateMatrix Rating matrix for each user (row) and item (column)
int	userCount The number of users.

Constructor Summary

BaselineRecommender(int uc, int ic, double max, double min)
Construct a constant model with the given data.

Method Summary

void	buildModel(SparseMatrix rm) Build a model with given training set.
EvaluationMetrics	evaluate(SparseMatrix testMatrix) Evaluate the designated algorithm with the given test data.
(package private) abstract double	predict(int userId, int itemId) Predict a rating for the given user and item.

Methods inherited from class java.lang.Object

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

Field Detail

rateMatrix

protected 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.

Constructor Detail

BaselineRecommender

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

Construct a constant 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 given training set.

Specified by:

buildModel in interface Recommender

Parameters:

rm - Training data set.

predict

abstract double predict(int userId,
                        int itemId)

Predict a rating for the given user and item.

Parameters:

userId - The target user.

itemId - The target item.

Returns:

predicted rating.

evaluate

public EvaluationMetrics evaluate(SparseMatrix testMatrix)

Evaluate the designated algorithm with the given test data.

Specified by:

evaluate in interface Recommender

Parameters:

testMatrix - The matrix with test data points used for evaluation.

Returns:

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