prea.recommender.memory
Class UserBased

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

All Implemented Interfaces:

Recommender

public class UserBased
extends MemoryBasedRecommender

The class implementing user-based neighborhood method, predicting by referring to rating matrix for each query.

Since:

2012. 4. 20

Version:

1.1

Author:

Joonseok Lee

Field Summary

SparseVector	userRateAverage Average of ratings for each user.
java.lang.String	userSimilarityFileName The name of pre-calculated user similarity file, if it is used.
boolean	userSimilarityPrefetch Indicating whether the pre-calculated user similarity file is used.

Fields inherited from class prea.recommender.memory.MemoryBasedRecommender

defaultValue, defaultVote, INVERSE_USER_FREQUENCY, itemCount, maxValue, MEAN_ABS_DIFF, MEAN_SQUARE_DIFF, minValue, neighborSize, PEARSON_CORR, rateMatrix, similarityMethod, SIMPLE_WEIGHTED_AVG, userCount, VECTOR_COS, WEIGHTED_SUM

Constructor Summary

UserBased(int uc, int ic, int max, int min, int ns, int sim, boolean df, double dv, SparseVector ura, boolean usp, java.lang.String usfn)
Construct a user-based model with the given data.

Method Summary

private double	estimation(int activeIndex, int targetIndex, int[] ref, int refCount, double[] refWeight, int method) Estimate a rating based on neighborhood data.
EvaluationMetrics	evaluate(SparseMatrix testMatrix) Evaluate the designated algorithm with the given test data.
private SparseVector	predict(int userNo, int[] testItemIndex, int k, double[] userSim) Predict ratings for a given user regarding given set of items, by user-based CF algorithm.

Methods inherited from class prea.recommender.memory.MemoryBasedRecommender

buildModel, similarity

Methods inherited from class java.lang.Object

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

Field Detail

userRateAverage

public SparseVector userRateAverage

Average of ratings for each user.

userSimilarityPrefetch

public boolean userSimilarityPrefetch

Indicating whether the pre-calculated user similarity file is used.

userSimilarityFileName

public java.lang.String userSimilarityFileName

The name of pre-calculated user similarity file, if it is used.

Constructor Detail

UserBased

public UserBased(int uc,
                 int ic,
                 int max,
                 int min,
                 int ns,
                 int sim,
                 boolean df,
                 double dv,
                 SparseVector ura,
                 boolean usp,
                 java.lang.String usfn)

Construct a user-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.

ura - The average of ratings for each user.

usp - Whether the pre-calculated user similarity file is used.

usfn - The name of pre-calculated user similarity file, if it is used.

Method Detail

evaluate

public EvaluationMetrics evaluate(SparseMatrix testMatrix)

Evaluate the designated algorithm with the given test data.

Parameters:

testMatrix - A rating matrix with test data.

Returns:

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

predict

private SparseVector predict(int userNo,
                             int[] testItemIndex,
                             int k,
                             double[] userSim)

Predict ratings for a given user regarding given set of items, by user-based CF algorithm.

Parameters:

userNo - The user ID.

testItemIndex - The list of items whose ratings will be predicted.

k - The neighborhood size.

userSim - The similarity vector between the target user and all the other users.

Returns:

The predicted ratings for each item.

estimation

private double estimation(int activeIndex,
                          int targetIndex,
                          int[] ref,
                          int refCount,
                          double[] refWeight,
                          int method)

Estimate a rating based on neighborhood data.

Parameters:

activeIndex - The active user index for user-based CF; The item index for item-based CF.

targetIndex - The target item index for user-based CF; The user index for item-based CF.

ref - The indices of neighborhood, which will be used for estimation.

refCount - The number of neighborhood, which will be used for estimation.

refWeight - The weight of each neighborhood.

method - The code of estimation method. It can be one of the following: WEIGHTED_SUM or SIMPLE_WEIGHTED_AVG.

Returns:

The estimated rating value.