net.librec.recommender.cf.rating

Class URPRecommender

java.lang.Object

net.librec.recommender.AbstractRecommender

net.librec.recommender.ProbabilisticGraphicalRecommender

net.librec.recommender.cf.rating.URPRecommender

All Implemented Interfaces:

Recommender

public class URPRecommender
extends ProbabilisticGraphicalRecommender

User Rating Profile: a LDA model for rating prediction.

Benjamin Marlin, Modeling user rating profiles for collaborative filtering, NIPS 2003.

Nicola Barbieri, Regularized gibbs sampling for user profiling with soft constraints, ASONAM 2011.

Author:

Guo Guibing and Haidong Zhang

Field Summary

Fields

Modifier and Type	Field and Description
protected int	numRatingLevels
protected int	numTopics number of topics
protected double[][][]	topicItemRatingProbs posterior probabilities of parameters phi_{k, i, r}
protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,java.lang.Integer>	topics

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
URPRecommender()

Method Summary

Methods

Modifier and Type	Method and Description
protected void	eStep() parameters estimation: used in the training phase
protected void	estimateParams() estimate the model parameters
protected boolean	isConverged(int iter) Post each iteration, we do things: print debug information check if converged if not, adjust learning rate
protected void	mStep() Thomas P.
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	readoutParams() read out parameters for each iteration
protected void	setup() setup init member method

Methods inherited from class net.librec.recommender.ProbabilisticGraphicalRecommender

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

topics

protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,java.lang.Integer> topics

numTopics

protected int numTopics

number of topics

numRatingLevels

protected int numRatingLevels

topicItemRatingProbs

protected double[][][] topicItemRatingProbs

posterior probabilities of parameters phi_{k, i, r}

Constructor Detail

URPRecommender

public URPRecommender()

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()

Thomas P. Minka, Estimating a Dirichlet distribution, see Eq.(55)

Specified by:

mStep in class ProbabilisticGraphicalRecommender

readoutParams

protected void readoutParams()

Description copied from class: ProbabilisticGraphicalRecommender

read out parameters for each iteration

Overrides:

readoutParams in class ProbabilisticGraphicalRecommender

estimateParams

protected void estimateParams()

Description copied from class: ProbabilisticGraphicalRecommender

estimate the model parameters

Overrides:

estimateParams in class ProbabilisticGraphicalRecommender

isConverged

protected boolean isConverged(int iter)

Description copied from class: AbstractRecommender

Post each iteration, we do things:

1. print debug information
2. check if converged
3. if not, adjust learning rate

Overrides:

isConverged in class ProbabilisticGraphicalRecommender

Parameters:

iter - current iteration

Returns:

boolean: true if it is converged; false otherwise

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