net.librec.recommender.cf.ranking

Class AspectModelRecommender

java.lang.Object

net.librec.recommender.AbstractRecommender

net.librec.recommender.ProbabilisticGraphicalRecommender

net.librec.recommender.cf.ranking.AspectModelRecommender

All Implemented Interfaces:

Recommender

public class AspectModelRecommender
extends ProbabilisticGraphicalRecommender

Latent class models for collaborative filtering

This implementation refers to the method proposed by Thomas et al. at IJCAI 1999.

Tempered EM: Thomas Hofmann, Latent class models for collaborative filtering , IJCAI. 1999, 99: 688-693.

Author:

Haidong Zhang and Keqiang Wang

Field Summary

Fields

Modifier and Type	Field and Description
protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,double[]>	entryTopicDistribution {user, item, {topic z, probability}}
protected int	numTopics number of topics
protected DenseMatrix	topicItemProbs Conditional distribution: P(i|z)
protected DenseMatrix	topicItemProbsSum Conditional distribution: P(i|z)
protected DenseVector	topicProbs topic distribution: P(z)
protected DenseVector	topicProbsSum topic distribution: P(z)
protected DenseMatrix	topicUserProbs Conditional distribution: P(u|z)
protected DenseMatrix	topicUserProbsSum Conditional distribution: P(u|z)

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
AspectModelRecommender()

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

Methods inherited from class net.librec.recommender.ProbabilisticGraphicalRecommender

estimateParams, isConverged, readoutParams, trainModel

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

number of topics

topicUserProbs

protected DenseMatrix topicUserProbs

Conditional distribution: P(u|z)

topicUserProbsSum

protected DenseMatrix topicUserProbsSum

Conditional distribution: P(u|z)

topicItemProbs

protected DenseMatrix topicItemProbs

Conditional distribution: P(i|z)

topicItemProbsSum

protected DenseMatrix topicItemProbsSum

Conditional distribution: P(i|z)

topicProbs

protected DenseVector topicProbs

topic distribution: P(z)

topicProbsSum

protected DenseVector topicProbsSum

topic distribution: P(z)

entryTopicDistribution

protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,double[]> entryTopicDistribution

{user, item, {topic z, probability}}

Constructor Detail

AspectModelRecommender

public AspectModelRecommender()

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

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