public class RFRecRecommender extends MatrixFactorizationRecommender
Remark: This implementation does not support half-star ratings.
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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RFRecRecommender() |
Modifier and Type | Method and Description |
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double |
predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
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protected void |
setup()
setup
init member method
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protected void |
trainModel()
train Model
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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 double predict(int userIdx, int itemIdx)
MatrixFactorizationRecommender
predict
in class MatrixFactorizationRecommender
userIdx
- user indexitemIdx
- item indexCopyright © 2017. All Rights Reserved.