net.librec.recommender.cf.rating

Class MFALSRecommender

java.lang.Object

net.librec.recommender.AbstractRecommender

net.librec.recommender.MatrixFactorizationRecommender

net.librec.recommender.cf.rating.MFALSRecommender

All Implemented Interfaces:

Recommender

@ModelData(value={"isRating","biasedMF","userFactors","itemFactors"})
public class MFALSRecommender
extends MatrixFactorizationRecommender

The class implementing the Alternating Least Squares algorithm

The origin paper: Yunhong Zhou, Dennis Wilkinson, Robert Schreiber and Rong Pan. Large-Scale Parallel Collaborative Filtering for the Netflix Prize. Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management. Shanghai, China pp. 337-348, 2008. http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/ netflix_aaim08(submitted).pdf

Author:

wubin (Email: wubin@gs.zzu.edu.cn)

Field Summary

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
MFALSRecommender()

Method Summary

Methods

Modifier and Type	Method and Description
protected void	trainModel() train Model

Methods inherited from class net.librec.recommender.MatrixFactorizationRecommender

predict, setup, 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

Constructor Detail

MFALSRecommender

public MFALSRecommender()

Method Detail

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