net.librec.recommender.cf.ranking

Class ItemBigramRecommender

java.lang.Object

net.librec.recommender.AbstractRecommender

net.librec.recommender.ProbabilisticGraphicalRecommender

net.librec.recommender.cf.ranking.ItemBigramRecommender

All Implemented Interfaces:

Recommender

@ModelData(value={"isRanking","itembigram","userTopicProbs","topicPreItemCurItemProbs"})
public class ItemBigramRecommender
extends ProbabilisticGraphicalRecommender

Hanna M. Wallach, Topic Modeling: Beyond Bag-of-Words, ICML 2006.

Author:

Keqiang Wang

Field Summary

Fields

Modifier and Type	Field and Description
protected DenseVector	alpha vector of hyperparameters for alpha
protected float	initAlpha Dirichlet hyper-parameters of user-topic distribution: typical value is 50/K
protected float	initBeta Dirichlet hyper-parameters of topic-item distribution, typical value is 0.01
protected int	numTopics number of topics
protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,java.lang.Integer>	topicAssignments entry[u, i, k]: topic assignment as sparse structure
protected DenseVector	userTokenNumbers entry[u]: number of tokens rated by user u.
protected DenseMatrix	userTopicNumbers entry[u, k]: number of tokens assigned to topic k, given user u.
protected DenseMatrix	userTopicProbs posterior probabilities of parameters
protected DenseMatrix	userTopicProbsSum cumulative statistics of theta, phi

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
ItemBigramRecommender()

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 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	readoutParams() read out parameters for each iteration
protected void	setup() setup init member method

Methods inherited from class net.librec.recommender.ProbabilisticGraphicalRecommender

isConverged, 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

alpha

protected DenseVector alpha

vector of hyperparameters for alpha

numTopics

protected int numTopics

number of topics

initAlpha

protected float initAlpha

Dirichlet hyper-parameters of user-topic distribution: typical value is 50/K

initBeta

protected float initBeta

Dirichlet hyper-parameters of topic-item distribution, typical value is 0.01

userTopicProbsSum

protected DenseMatrix userTopicProbsSum

cumulative statistics of theta, phi

userTopicNumbers

protected DenseMatrix userTopicNumbers

entry[u, k]: number of tokens assigned to topic k, given user u.

userTokenNumbers

protected DenseVector userTokenNumbers

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

userTopicProbs

protected DenseMatrix userTopicProbs

posterior probabilities of parameters

topicAssignments

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

entry[u, i, k]: topic assignment as sparse structure

Constructor Detail

ItemBigramRecommender

public ItemBigramRecommender()

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

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

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