public class LLORMARecommender extends MatrixFactorizationRecommender
This implementation refers to the method proposed by Lee et al. at ICML 2013.
Lcoal Structure: Joonseok Lee, Local Low-Rank Matrix Approximation , ICML. 2013: 82-90.
Modifier and Type | Field and Description |
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protected double |
globalRegItem |
protected double |
globalRegUser |
protected double |
localRegItem |
protected double |
localRegUser |
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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LLORMARecommender() |
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 globalRegUser
protected double globalRegItem
protected double localRegUser
protected double localRegItem
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)
MatrixFactorizationRecommender
predict
in class MatrixFactorizationRecommender
userIdx
- user indexitemIdx
- item indexCopyright © 2017. All Rights Reserved.