@ModelData(value={"isRating","socialmf","userFactors","itemFactors"}) public class SocialMFRecommender extends SocialRecommender
regSocial, socialMatrixinitMean, initStd, itemFactors, learnRate, maxLearnRate, numFactors, numIterations, regItem, regUser, userFactorsconf, 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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SocialMFRecommender() |
| Modifier and Type | Method and Description |
|---|---|
void |
setup()
setup
init member method
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protected void |
trainModel()
train Model
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denormalize, normalize, predictpredict, updateLRatecleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, recommend, recommend, recommendRank, recommendRating, saveModel, setContextpublic void setup()
throws LibrecException
MatrixFactorizationRecommendersetup in class SocialRecommenderLibrecException - if error occurs during setting upprotected void trainModel()
throws LibrecException
AbstractRecommendertrainModel in class AbstractRecommenderLibrecException - if error occurs during training modelCopyright © 2017. All Rights Reserved.