public class EFMRecommender extends TensorRecommender
Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 2014: 83-92
.Modifier and Type | Field and Description |
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protected int |
featureFactor |
protected DenseMatrix |
featureMatrix |
com.google.common.collect.BiMap<java.lang.String,java.lang.Integer> |
featureSentimemtPairsMappingData |
protected float |
initMean
init mean
|
protected float |
initStd
init standard deviation
|
protected DenseMatrix |
itemFactors
item latent factors
|
protected DenseMatrix |
itemFeatureMatrix |
protected DenseMatrix |
itemFeatureQuality |
protected DenseMatrix |
itemHiddenMatrix |
protected double |
lambdaH |
protected double |
lambdaU |
protected double |
lambdaV |
protected double |
lambdaX |
protected double |
lambdaY |
protected int |
numberOfFeatures |
protected int |
scoreScale |
protected SparseMatrix |
trainMatrix |
protected DenseMatrix |
userFactors
user latent factors
|
protected DenseMatrix |
userFeatureAttention |
protected DenseMatrix |
userFeatureMatrix |
protected DenseMatrix |
userHiddenMatrix |
allFeaturesMappingData, conf, context, decay, dimensions, earlyStop, globalMean, isBoldDriver, isRanking, itemDimension, itemMappingData, lastLoss, learnRate, LOG, loss, maxLearnRate, maxRate, minRate, numDimensions, numFactors, numItems, numIterations, numUsers, recommendedList, reg, testTensor, topN, trainTensor, userDimension, userMappingData, validTensor, verbose
Constructor and Description |
---|
EFMRecommender() |
Modifier and Type | Method and Description |
---|---|
java.util.Map<Measure.MeasureValue,java.lang.Double> |
evaluateMap()
evaluate Map
|
java.util.List<RecommendedItem> |
getRecommendedList()
get Recommended List
|
protected double |
predict(int[] indices)
predict a specific rating for user userIdx on item itemIdx with some other contexts indices, note that the
prediction is not bounded.
|
protected double |
predict(int u,
int j) |
protected void |
setup()
setup
|
protected void |
trainModel()
train Model
|
cleanup, evaluate, getDataModel, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext, updateLRate
protected int numberOfFeatures
protected int featureFactor
protected int scoreScale
protected DenseMatrix featureMatrix
protected DenseMatrix userFeatureMatrix
protected DenseMatrix userHiddenMatrix
protected DenseMatrix itemFeatureMatrix
protected DenseMatrix itemHiddenMatrix
protected DenseMatrix userFeatureAttention
protected DenseMatrix itemFeatureQuality
protected double lambdaX
protected double lambdaY
protected double lambdaU
protected double lambdaH
protected double lambdaV
protected DenseMatrix userFactors
protected DenseMatrix itemFactors
protected float initMean
protected float initStd
protected SparseMatrix trainMatrix
public com.google.common.collect.BiMap<java.lang.String,java.lang.Integer> featureSentimemtPairsMappingData
protected void setup() throws LibrecException
TensorRecommender
setup
in class TensorRecommender
LibrecException
- if error occurs during setting upprotected void trainModel() throws LibrecException
TensorRecommender
trainModel
in class TensorRecommender
LibrecException
- if error occurs during trainingprotected double predict(int[] indices)
TensorRecommender
predict
in class TensorRecommender
indices
- user index, item index and context indicesprotected double predict(int u, int j)
public java.util.Map<Measure.MeasureValue,java.lang.Double> evaluateMap() throws LibrecException
Recommender
evaluateMap
in interface Recommender
evaluateMap
in class TensorRecommender
LibrecException
- if error occurs during constructing evaluate mappublic java.util.List<RecommendedItem> getRecommendedList()
Recommender
getRecommendedList
in interface Recommender
getRecommendedList
in class TensorRecommender
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