@ModelData(value={"isRating","nmf","transUserFactors","transItemFactors"}) public class NMFRecommender extends AbstractRecommender
Modifier and Type | Field and Description |
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protected int |
numFactors
the number of latent factors;
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protected int |
numIterations
the number of iterations
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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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NMFRecommender() |
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.
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protected void |
setup()
setup
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protected void |
trainModel()
train Model
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cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
protected int numFactors
protected int numIterations
protected void setup() throws LibrecException
AbstractRecommender
setup
in class AbstractRecommender
LibrecException
- if error occurs during setupprotected 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 AbstractRecommender
userIdx
- user indexitemIdx
- item indexLibrecException
- if error occursCopyright © 2017. All Rights Reserved.