@ModelData(value={"isRanking","itembigram","userTopicProbs","topicPreItemCurItemProbs"}) public class ItemBigramRecommender extends ProbabilisticGraphicalRecommender
Modifier and Type | Field and Description |
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protected DenseVector |
alpha
vector of hyperparameters for alpha
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protected float |
initAlpha
Dirichlet hyper-parameters of user-topic distribution: typical value is 50/K
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protected float |
initBeta
Dirichlet hyper-parameters of topic-item distribution, typical value is 0.01
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protected int |
numTopics
number of topics
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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
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protected DenseVector |
userTokenNumbers
entry[u]: number of tokens rated by user u.
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protected DenseMatrix |
userTopicNumbers
entry[u, k]: number of tokens assigned to topic k, given user u.
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protected DenseMatrix |
userTopicProbs
posterior probabilities of parameters
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protected DenseMatrix |
userTopicProbsSum
cumulative statistics of theta, phi
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burnIn, numItems, numIterations, numStats, numUsers, sampleLag
conf, context, decay, earlyStop, globalMean, isBoldDriver, isRanking, itemMappingData, lastLoss, LOG, loss, maxRate, minRate, numRates, ratingScale, recommendedList, testMatrix, topN, trainMatrix, userMappingData, validMatrix, verbose
Constructor and Description |
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ItemBigramRecommender() |
Modifier and Type | Method and Description |
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protected void |
eStep()
parameters estimation: used in the training phase
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protected void |
estimateParams()
estimate the model parameters
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protected void |
mStep()
update the hyper-parameters
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protected double |
predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx, note that the
prediction is not bounded.
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protected void |
readoutParams()
read out parameters for each iteration
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protected void |
setup()
setup
init member method
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isConverged, trainModel
cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
protected DenseVector alpha
protected int numTopics
protected float initAlpha
protected float initBeta
protected DenseMatrix userTopicProbsSum
protected DenseMatrix userTopicNumbers
protected DenseVector userTokenNumbers
protected DenseMatrix userTopicProbs
protected com.google.common.collect.Table<java.lang.Integer,java.lang.Integer,java.lang.Integer> topicAssignments
protected void setup() throws LibrecException
ProbabilisticGraphicalRecommender
setup
in class ProbabilisticGraphicalRecommender
LibrecException
- if error occurs during setting upprotected void eStep()
ProbabilisticGraphicalRecommender
eStep
in class ProbabilisticGraphicalRecommender
protected void mStep()
ProbabilisticGraphicalRecommender
mStep
in class ProbabilisticGraphicalRecommender
protected void readoutParams()
ProbabilisticGraphicalRecommender
readoutParams
in class ProbabilisticGraphicalRecommender
protected void estimateParams()
ProbabilisticGraphicalRecommender
estimateParams
in class ProbabilisticGraphicalRecommender
protected double predict(int userIdx, int itemIdx) throws LibrecException
AbstractRecommender
predict
in class AbstractRecommender
userIdx
- user indexitemIdx
- item indexLibrecException
- if error occurs during predictingCopyright © 2017. All Rights Reserved.