@ModelData(value={"isRating","biasedMF","userFactors","itemFactors","userBiases","itemBiases"}) public class BiasedMFRecommender extends MatrixFactorizationRecommender
Modifier and Type | Field and Description |
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protected DenseVector |
itemBiases
user biases
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protected double |
regBias
bias regularization
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protected DenseVector |
userBiases
user biases
|
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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BiasedMFRecommender() |
Modifier and Type | Method and Description |
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protected double |
predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected void |
setup()
setup
init member method
|
protected void |
trainModel()
train Model
|
updateLRate
cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
protected double regBias
protected DenseVector userBiases
protected DenseVector itemBiases
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 modelprotected double predict(int userIdx, int itemIdx) throws LibrecException
predict
in class MatrixFactorizationRecommender
userIdx
- user indexitemIdx
- item indexLibrecException
- if error occursCopyright © 2017. All Rights Reserved.