prea.recommender
Class CustomRecommender

java.lang.Object
  extended by prea.recommender.CustomRecommender
All Implemented Interfaces:
Recommender

public class CustomRecommender
extends java.lang.Object
implements Recommender

This is a skeleton class for user-defined custom recommenders. You may copy this class if you want to implement more than one class. Please look at the "COMMENT FOR AUTHORS" in each method carefully before coding your algorithm.

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.
 SparseMatrix rateMatrix
          Rating matrix for each user (row) and item (column)
 int userCount
          The number of users.
 
Constructor Summary
CustomRecommender(int uc, int ic, double max, double min)
          Construct a customized recommender 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.
 
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.

Constructor Detail

CustomRecommender

public CustomRecommender(int uc,
                         int ic,
                         double max,
                         double min)
Construct a customized recommender 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.