@ModelData(value={"isRanking","gbpr","userFactors","itemFactors","trainMatrix"}) public class GBPRRecommender extends MatrixFactorizationRecommender
Modifier and Type | Field and Description |
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protected static java.lang.String |
cacheSpec
Guava cache configuration
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protected com.google.common.cache.LoadingCache<java.lang.Integer,java.util.List<java.lang.Integer>> |
itemUsersCache
user-items cache, item-users cache
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protected double |
regBias
bias regularization
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protected com.google.common.cache.LoadingCache<java.lang.Integer,java.util.List<java.lang.Integer>> |
userItemsCache
user-items cache, item-users cache
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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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GBPRRecommender() |
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 double |
predict(int userIdx,
int itemIdx,
java.util.Set<java.lang.Integer> groupSet) |
protected void |
setup()
setup
init member method
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protected void |
trainModel()
train Model
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updateLRate
cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
protected double regBias
protected com.google.common.cache.LoadingCache<java.lang.Integer,java.util.List<java.lang.Integer>> userItemsCache
protected com.google.common.cache.LoadingCache<java.lang.Integer,java.util.List<java.lang.Integer>> itemUsersCache
protected static java.lang.String cacheSpec
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, java.util.Set<java.lang.Integer> groupSet) throws LibrecException
LibrecException
protected double predict(int userIdx, int itemIdx)
MatrixFactorizationRecommender
predict
in class MatrixFactorizationRecommender
userIdx
- user indexitemIdx
- item indexCopyright © 2017. All Rights Reserved.