net.librec.recommender.cf.ranking

Class WRMFRecommender

java.lang.Object

net.librec.recommender.AbstractRecommender

net.librec.recommender.MatrixFactorizationRecommender

net.librec.recommender.cf.ranking.WRMFRecommender

All Implemented Interfaces:

Recommender

@ModelData(value={"isRanking","wrmf","userFactors","itemFactors","trainMatrix"})
public class WRMFRecommender
extends MatrixFactorizationRecommender

WRMF: Weighted Regularized Matrix Factorization.

This implementation refers to the method proposed by Hu et al. at ICDM 2008.

• Binary ratings: Pan et al., One-class Collaborative Filtering, ICDM 2008.
• Real ratings: Hu et al., Collaborative filtering for implicit feedback datasets, ICDM 2008.

Author:

guoguibing and Keqiang Wang

Field Summary

Fields

Modifier and Type	Field and Description
protected SparseMatrix	confindenceMinusIdentityMatrix confindence Minus Identity Matrix{ui} = confidenceMatrix_{ui} - 1 =alpha * r_{ui} or log(1+10^alpha * r_{ui})
protected SparseMatrix	preferenceMatrix preferenceMatrix_{ui} = 1 if r_{ui}>0 or preferenceMatrix_{ui} = 0
protected float	weightCoefficient confidence weight coefficient

Fields inherited from class net.librec.recommender.MatrixFactorizationRecommender

initMean, initStd, itemFactors, learnRate, maxLearnRate, numFactors, numIterations, regItem, regUser, userFactors

Fields inherited from class net.librec.recommender.AbstractRecommender

conf, context, decay, earlyStop, globalMean, isBoldDriver, isRanking, itemMappingData, lastLoss, LOG, loss, maxRate, minRate, numItems, numRates, numUsers, ratingScale, recommendedList, testMatrix, topN, trainMatrix, userMappingData, validMatrix, verbose

Constructor Summary

Constructors

Constructor and Description
WRMFRecommender()

Method Summary

Methods

Modifier and Type	Method and Description
protected void	setup() setup init member method
protected void	trainModel() train Model

Methods inherited from class net.librec.recommender.MatrixFactorizationRecommender

predict, updateLRate

Methods inherited from class net.librec.recommender.AbstractRecommender

cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext

Methods inherited from class java.lang.Object

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

Field Detail

weightCoefficient

protected float weightCoefficient

confidence weight coefficient

confindenceMinusIdentityMatrix

protected SparseMatrix confindenceMinusIdentityMatrix

confindence Minus Identity Matrix{ui} = confidenceMatrix_{ui} - 1 =alpha * r_{ui} or log(1+10^alpha * r_{ui})

preferenceMatrix

protected SparseMatrix preferenceMatrix

preferenceMatrix_{ui} = 1 if r_{ui}>0 or preferenceMatrix_{ui} = 0

Constructor Detail

WRMFRecommender

public WRMFRecommender()

Method Detail

setup

protected void setup()
              throws LibrecException

Description copied from class: MatrixFactorizationRecommender

setup init member method

Overrides:

setup in class MatrixFactorizationRecommender

Throws:

LibrecException - if error occurs during setting up

trainModel

protected void trainModel()
                   throws LibrecException

Description copied from class: AbstractRecommender

train Model

Specified by:

trainModel in class AbstractRecommender

Throws:

LibrecException - if error occurs during training model