@ModelData(value={"isRating","rste","userFactors","itemFactors","userSocialRatio","socialMatrix"}) public class RSTERecommender extends SocialRecommender
This method is quite time-consuming when dealing with the social influence part.
regSocial, socialMatrix
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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RSTERecommender() |
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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void |
setup()
setup
init member method
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protected void |
trainModel()
train Model
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denormalize, normalize, predict
updateLRate
cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
public void setup() throws LibrecException
MatrixFactorizationRecommender
setup
in class SocialRecommender
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.