public class ListRankMFRecommender extends MatrixFactorizationRecommender
Modifier and Type | Field and Description |
---|---|
DenseVector |
userExp |
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 |
---|
ListRankMFRecommender() |
Modifier and Type | Method and Description |
---|---|
double |
getLoss(DenseMatrix userFactors,
DenseMatrix itemFactors) |
protected void |
setup()
setup
init member method
|
protected void |
trainModel()
train Model
|
predict, updateLRate
cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
public DenseVector userExp
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 getLoss(DenseMatrix userFactors, DenseMatrix itemFactors)
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