net.librec.recommender.cf.rating

Class GPLSARecommender

java.lang.Object

net.librec.recommender.AbstractRecommender

net.librec.recommender.ProbabilisticGraphicalRecommender

net.librec.recommender.cf.rating.GPLSARecommender

All Implemented Interfaces:

Recommender

public class GPLSARecommender
extends ProbabilisticGraphicalRecommender

Thomas Hofmann, Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis, SIGIR 2003.

Tempered EM: Thomas Hofmann, Unsupervised Learning by Probabilistic Latent Semantic Analysis, Machine Learning, 42, 177�C196, 2001.

Field Summary

Fields

Modifier and Type	Field and Description
protected float	b
protected int	numTopics
protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,java.util.Map<java.lang.Integer,java.lang.Double>>	Q
protected static double	smallValue
protected float	smoothWeight
protected DenseMatrix	topicItemMu
protected DenseMatrix	topicItemSigma
protected DenseVector	userMu
protected DenseVector	userSigma
protected DenseMatrix	userTopicProbs

Fields inherited from class net.librec.recommender.ProbabilisticGraphicalRecommender

burnIn, numItems, numIterations, numStats, numUsers, sampleLag

Fields inherited from class net.librec.recommender.AbstractRecommender

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

Constructor Summary

Constructors

Constructor and Description
GPLSARecommender()

Method Summary

Methods

Modifier and Type	Method and Description
protected void	eStep() parameters estimation: used in the training phase
protected void	mStep() update the hyper-parameters
protected double	predict(int userIdx, int itemIdx) predict a specific rating for user userIdx on item itemIdx, note that the prediction is not bounded.
protected void	setup() setup init member method
protected void	trainModel() train Model

Methods inherited from class net.librec.recommender.ProbabilisticGraphicalRecommender

estimateParams, isConverged, readoutParams

Methods inherited from class net.librec.recommender.AbstractRecommender

cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, 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

numTopics

protected int numTopics

Q

protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,java.util.Map<java.lang.Integer,java.lang.Double>> Q

userTopicProbs

protected DenseMatrix userTopicProbs

topicItemMu

protected DenseMatrix topicItemMu

topicItemSigma

protected DenseMatrix topicItemSigma

userMu

protected DenseVector userMu

userSigma

protected DenseVector userSigma

smoothWeight

protected float smoothWeight

b

protected float b

smallValue

protected static double smallValue

Constructor Detail

GPLSARecommender

public GPLSARecommender()

Method Detail

setup

protected void setup()
              throws LibrecException

Description copied from class: ProbabilisticGraphicalRecommender

setup init member method

Overrides:

setup in class ProbabilisticGraphicalRecommender

Throws:

LibrecException - if error occurs during setting up

trainModel

protected void trainModel()
                   throws LibrecException

Description copied from class: AbstractRecommender

train Model

Overrides:

trainModel in class ProbabilisticGraphicalRecommender

Throws:

LibrecException - if error occurs during training model

eStep

protected void eStep()

Description copied from class: ProbabilisticGraphicalRecommender

parameters estimation: used in the training phase

Specified by:

eStep in class ProbabilisticGraphicalRecommender

mStep

protected void mStep()

Description copied from class: ProbabilisticGraphicalRecommender

update the hyper-parameters

Specified by:

mStep in class ProbabilisticGraphicalRecommender

predict

protected double predict(int userIdx,
             int itemIdx)
                  throws LibrecException

Description copied from class: AbstractRecommender

predict a specific rating for user userIdx on item itemIdx, note that the prediction is not bounded. It is useful for building models with no need to bound predictions.

Specified by:

predict in class AbstractRecommender

Parameters:

userIdx - user index

itemIdx - item index

Returns:

predictive rating for user userIdx on item itemIdx without bound

Throws:

LibrecException - if error occurs during predicting