net.librec.recommender.cf.ranking

Class PLSARecommender

java.lang.Object

net.librec.recommender.AbstractRecommender

net.librec.recommender.ProbabilisticGraphicalRecommender

net.librec.recommender.cf.ranking.PLSARecommender

All Implemented Interfaces:

Recommender

public class PLSARecommender
extends ProbabilisticGraphicalRecommender

Thomas Hofmann, Latent semantic models for collaborative filtering, ACM Transactions on Information Systems. 2004.

Author:

Haidong Zhang and Keqiang Wang

Field Summary

Fields

Modifier and Type	Field and Description
protected DenseVector	numItemsRateByUser entry[u]: number of tokens rated by user u.
protected int	numTopics number of latent topics
protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,double[]>	Q {user, item, {topic z, probability}}
protected DenseMatrix	topicItemProbs Conditional Probability: P(i|z)
protected DenseMatrix	topicItemProbsSum Conditional Probability: P(i|z)
protected DenseMatrix	userTopicProbs Conditional Probability: P(z|u)
protected DenseMatrix	userTopicProbsSum Conditional Probability: P(z|u)

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
PLSARecommender()

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 latent topics

Q

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

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

userTopicProbs

protected DenseMatrix userTopicProbs

Conditional Probability: P(z|u)

userTopicProbsSum

protected DenseMatrix userTopicProbsSum

Conditional Probability: P(z|u)

topicItemProbs

protected DenseMatrix topicItemProbs

Conditional Probability: P(i|z)

topicItemProbsSum

protected DenseMatrix topicItemProbsSum

Conditional Probability: P(i|z)

numItemsRateByUser

protected DenseVector numItemsRateByUser

entry[u]: number of tokens rated by user u.

Constructor Detail

PLSARecommender

public PLSARecommender()

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