@ModelData(value={"isRanking","aobpr","userFactors","itemFactors"}) public class AoBPRRecommender extends MatrixFactorizationRecommender
Rendle and Freudenthaler, Improving pairwise learning for item recommendation from implicit feedback, WSDM 2014.
initMean, initStd, itemFactors, learnRate, maxLearnRate, numFactors, numIterations, regItem, regUser, userFactors
conf, context, decay, earlyStop, globalMean, isBoldDriver, isRanking, itemMappingData, lastLoss, LOG, loss, maxRate, minRate, numItems, numRates, numUsers, ratingScale, recommendedList, testMatrix, topN, trainMatrix, userMappingData, validMatrix, verbose
Constructor and Description |
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AoBPRRecommender() |
Modifier and Type | Method and Description |
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protected void |
setup()
setup
init member method
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java.util.List<java.util.Map.Entry<java.lang.Integer,java.lang.Double>> |
sortByDenseVectorValue(DenseVector vector) |
protected void |
trainModel()
train Model
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void |
updateRankingInFactor() |
predict, updateLRate
cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
protected void setup() throws LibrecException
MatrixFactorizationRecommender
setup
in class MatrixFactorizationRecommender
LibrecException
- if error occurs during setting upprotected void trainModel() throws LibrecException
AbstractRecommender
trainModel
in class AbstractRecommender
LibrecException
- if error occurs during training modelpublic java.util.List<java.util.Map.Entry<java.lang.Integer,java.lang.Double>> sortByDenseVectorValue(DenseVector vector)
public void updateRankingInFactor()
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