Modifier and Type | Method and Description |
---|---|
void |
DataModel.buildDataModel()
Build data model.
|
void |
DataModel.loadDataModel()
Load data model.
|
void |
DataModel.saveDataModel()
Save data model.
|
void |
DataSplitter.splitData()
Split the data.
|
Modifier and Type | Method and Description |
---|---|
void |
TextDataModel.buildConvert()
Build Convert.
|
protected void |
JDBCDataModel.buildConvert() |
void |
ArffDataModel.buildConvert()
Build model.
|
protected abstract void |
AbstractDataModel.buildConvert()
Build Convert.
|
void |
AbstractDataModel.buildDataModel()
Build data model.
|
protected void |
AbstractDataModel.buildFeature()
Build appender data.
|
protected void |
ArffDataModel.buildSplitter()
Build Splitter.
|
protected void |
AbstractDataModel.buildSplitter()
Build Splitter.
|
java.lang.Object |
ArffInstance.getValueByAttrName(java.lang.String attrName)
Get data value by the attribute name.
|
void |
TextDataModel.loadDataModel()
Load data model.
|
void |
AbstractDataModel.loadDataModel()
Load data model.
|
void |
TextDataModel.saveDataModel()
Save data model.
|
void |
AbstractDataModel.saveDataModel()
Save data model.
|
Modifier and Type | Method and Description |
---|---|
void |
RatioDataSplitter.splitData()
Split the dataset according to the configuration file.
|
void |
LOOCVDataSplitter.splitData()
Split the data.
|
void |
KCVDataSplitter.splitData()
Split the data.
|
void |
GivenTestSetDataSplitter.splitData()
Split the data.
|
void |
GivenNDataSplitter.splitData()
Split the data.
|
void |
KCVDataSplitter.splitData(int k)
preserve the k-th validation as the test set and the rest as train set
|
Modifier and Type | Method and Description |
---|---|
void |
RecommenderJob.runJob()
run Job
|
void |
RecommenderJob.saveResult(java.util.List<RecommendedItem> recommendedList)
Save result.
|
Modifier and Type | Method and Description |
---|---|
static DenseMatrix |
Randoms.wishart(DenseMatrix scale,
double df)
Randomly sample a matrix from Wishart Distribution with the given parameters.
|
Modifier and Type | Method and Description |
---|---|
DenseMatrix |
DenseMatrix.add(DenseMatrix mat)
Do
A + B matrix operation |
DenseMatrix |
DenseMatrix.add(SparseMatrix mat)
Do
A + B matrix operation |
DenseMatrix |
DenseMatrix.addEqual(DenseMatrix mat)
Do
A + B matrix operation |
DenseMatrix |
DenseMatrix.addEqual(SparseMatrix mat)
Do
A + B matrix operation |
DenseMatrix |
DenseMatrix.minus(DenseMatrix mat)
Do
A - B matrix operation |
DenseMatrix |
DenseMatrix.minus(SparseMatrix mat)
Do
A - B matrix operation |
DenseMatrix |
DenseMatrix.minusEqual(DenseMatrix mat)
Do
A - B matrix operation |
DenseMatrix |
DenseMatrix.minusEqual(SparseMatrix mat)
Do
A - B matrix operation |
DenseMatrix |
DenseMatrix.mult(DenseMatrix mat)
Matrix multiplication with a dense matrix
|
DenseVector |
DenseMatrix.mult(DenseVector vec)
Do
matrix x vector between current matrix and a given vector |
DenseMatrix |
DenseMatrix.mult(SparseMatrix mat)
Matrix multiplication with a sparse matrix
|
static DenseMatrix |
DenseMatrix.mult(SparseMatrix sm,
DenseMatrix dm)
Matrix multiplication of a sparse matrix by a dense matrix
|
DenseMatrix |
DenseMatrix.pinv() |
static double |
DenseMatrix.product(DenseMatrix m,
int mrow,
DenseMatrix n,
int ncol)
Dot product of row x col between two matrices.
|
Modifier and Type | Method and Description |
---|---|
protected void |
TensorRecommender.cleanup()
cleanup
|
protected void |
AbstractRecommender.cleanup()
cleanup
|
double |
TensorRecommender.evaluate(RecommenderEvaluator evaluator) |
double |
Recommender.evaluate(RecommenderEvaluator evaluator)
evaluate
|
double |
AbstractRecommender.evaluate(RecommenderEvaluator evaluator)
evaluate
|
java.util.Map<Measure.MeasureValue,java.lang.Double> |
TensorRecommender.evaluateMap() |
java.util.Map<Measure.MeasureValue,java.lang.Double> |
Recommender.evaluateMap()
evaluate Map
|
java.util.Map<Measure.MeasureValue,java.lang.Double> |
AbstractRecommender.evaluateMap()
evaluate Map
|
protected boolean |
TensorRecommender.isConverged(int iter)
Post each iteration, we do things:
print debug information
check if converged
if not, adjust learning rate
|
protected boolean |
AbstractRecommender.isConverged(int iter)
Post each iteration, we do things:
print debug information
check if converged
if not, adjust learning rate
|
protected abstract double |
TensorRecommender.predict(int[] keys)
predict a specific rating for user userIdx on item itemIdx with some other contexts indices, note that the
prediction is not bounded.
|
protected double |
TensorRecommender.predict(int[] keys,
boolean bound)
predict a specific rating for user userIdx on item itemIdx with some other contexts indices.
|
protected double |
MatrixFactorizationRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected abstract double |
AbstractRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx, note that the
prediction is not bounded.
|
protected double |
SocialRecommender.predict(int userIdx,
int itemIdx,
boolean bounded) |
protected double |
AbstractRecommender.predict(int userIdx,
int itemIdx,
boolean bound)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
FactorizationMachineRecommender.predict(int userId,
int itemId,
SparseVector x)
Predict the rating given a sparse appender vector.
|
protected double |
FactorizationMachineRecommender.predict(int userId,
int itemId,
SparseVector x,
boolean bound)
Predict the rating given a sparse appender vector.
|
protected RecommendedList |
TensorRecommender.recommend()
recommend
* predict the ranking scores or ratings in the test data
|
protected RecommendedList |
AbstractRecommender.recommend()
recommend
* predict the ranking scores or ratings in the test data
|
void |
TensorRecommender.recommend(RecommenderContext context)
recommend
|
void |
Recommender.recommend(RecommenderContext context)
recommend
|
void |
AbstractRecommender.recommend(RecommenderContext context)
recommend
|
protected RecommendedList |
TensorRecommender.recommendRank()
recommend
* predict the ranking scores in the test data
|
protected RecommendedList |
AbstractRecommender.recommendRank()
recommend
* predict the ranking scores in the test data
|
protected RecommendedList |
TensorRecommender.recommendRating()
recommend
* predict the ratings in the test data
|
protected RecommendedList |
FactorizationMachineRecommender.recommendRating()
recommend
* predict the ratings in the test data
|
protected RecommendedList |
AbstractRecommender.recommendRating()
recommend
* predict the ratings in the test data
|
protected void |
TensorRecommender.setup()
setup
|
void |
SocialRecommender.setup() |
protected void |
ProbabilisticGraphicalRecommender.setup()
setup
init member method
|
protected void |
MatrixFactorizationRecommender.setup()
setup
init member method
|
protected void |
FactorizationMachineRecommender.setup()
setup
|
protected void |
AbstractRecommender.setup()
setup
|
protected abstract void |
TensorRecommender.trainModel()
train Model
|
protected void |
ProbabilisticGraphicalRecommender.trainModel() |
protected abstract void |
AbstractRecommender.trainModel()
train Model
|
Modifier and Type | Method and Description |
---|---|
protected double |
UserClusterRecommender.predict(int userIdx,
int itemIdx) |
protected double |
UserAverageRecommender.predict(int userIdx,
int itemIdx)
the user ratings average value as the predictive rating for user userIdx on item itemIdx.
|
protected double |
RandomGuessRecommender.predict(int userIdx,
int itemIdx)
a random value as the predictive rating for user userIdx on item itemIdx.
|
protected double |
MostPopularRecommender.predict(int userIdx,
int itemIdx)
The rated count as the predictive ranking score for user userIdx on item itemIdx.
|
protected double |
ItemClusterRecommender.predict(int userIdx,
int itemIdx) |
protected double |
ItemAverageRecommender.predict(int userIdx,
int itemIdx)
the item ratings average value as the predictive rating for user userIdx on item itemIdx.
|
protected double |
GlobalAverageRecommender.predict(int userIdx,
int itemIdx)
the global average value as the predictive rating for user userIdx on item itemIdx.
|
protected void |
UserClusterRecommender.setup() |
protected void |
UserAverageRecommender.setup() |
protected void |
RandomGuessRecommender.setup() |
protected void |
MostPopularRecommender.setup() |
protected void |
ItemClusterRecommender.setup() |
protected void |
ItemAverageRecommender.setup() |
protected void |
UserAverageRecommender.trainModel() |
protected void |
RandomGuessRecommender.trainModel() |
protected void |
MostPopularRecommender.trainModel() |
protected void |
ItemAverageRecommender.trainModel() |
protected void |
GlobalAverageRecommender.trainModel() |
protected void |
ConstantGuessRecommender.trainModel() |
Modifier and Type | Method and Description |
---|---|
double |
UserKNNRecommender.predict(int userIdx,
int itemIdx)
(non-Javadoc)
|
double |
ItemKNNRecommender.predict(int userIdx,
int itemIdx)
(non-Javadoc)
|
protected double |
BUCMRecommender.predict(int userIdx,
int itemIdx) |
protected double |
BHFreeRecommender.predict(int userIdx,
int itemIdx) |
protected void |
UserKNNRecommender.setup()
(non-Javadoc)
|
protected void |
ItemKNNRecommender.setup()
(non-Javadoc)
|
protected void |
BUCMRecommender.setup() |
protected void |
BHFreeRecommender.setup() |
protected void |
UserKNNRecommender.trainModel()
(non-Javadoc)
|
protected void |
ItemKNNRecommender.trainModel()
(non-Javadoc)
|
Modifier and Type | Method and Description |
---|---|
protected double |
WBPRRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
SLIMRecommender.predict(int userIdx,
int itemIdx)
predict a specific ranking score for user userIdx on item itemIdx.
|
protected double |
PLSARecommender.predict(int userIdx,
int itemIdx) |
protected double |
LDARecommender.predict(int userIdx,
int itemIdx) |
protected double |
ItemBigramRecommender.predict(int userIdx,
int itemIdx) |
protected double |
FISMrmseRecommender.predict(int u,
int j) |
protected double |
FISMaucRecommender.predict(int u,
int j) |
protected double |
AspectModelRecommender.predict(int userIdx,
int itemIdx) |
protected double |
GBPRRecommender.predict(int userIdx,
int itemIdx,
java.util.Set<java.lang.Integer> groupSet) |
protected void |
WRMFRecommender.setup() |
protected void |
WBPRRecommender.setup() |
protected void |
SLIMRecommender.setup()
initialization
|
protected void |
RankSGDRecommender.setup() |
protected void |
RankALSRecommender.setup() |
protected void |
PLSARecommender.setup() |
protected void |
ListRankMFRecommender.setup() |
protected void |
LDARecommender.setup()
setup
init member method
|
protected void |
ItemBigramRecommender.setup() |
protected void |
GBPRRecommender.setup() |
protected void |
FISMrmseRecommender.setup() |
protected void |
FISMaucRecommender.setup() |
protected void |
EALSRecommender.setup() |
protected void |
CLIMFRecommender.setup() |
protected void |
BPRRecommender.setup() |
protected void |
AspectModelRecommender.setup() |
protected void |
AoBPRRecommender.setup() |
protected void |
WRMFRecommender.trainModel() |
protected void |
WBPRRecommender.trainModel() |
protected void |
SLIMRecommender.trainModel()
train model
|
protected void |
RankSGDRecommender.trainModel() |
protected void |
RankALSRecommender.trainModel() |
protected void |
ListRankMFRecommender.trainModel() |
protected void |
GBPRRecommender.trainModel() |
protected void |
FISMrmseRecommender.trainModel() |
protected void |
FISMaucRecommender.trainModel() |
protected void |
EALSRecommender.trainModel() |
protected void |
CLIMFRecommender.trainModel() |
protected void |
BPRRecommender.trainModel() |
protected void |
AoBPRRecommender.trainModel() |
Modifier and Type | Method and Description |
---|---|
protected DenseVector |
BPoissMFRecommender.getPhi(DenseMatrix Theta,
int indexTheta,
DenseMatrix Beta,
int indexBeta,
int number) |
protected void |
BPMFRecommender.initModel()
Initialize the model
|
protected double |
URPRecommender.predict(int userIdx,
int itemIdx) |
protected double |
SVDPlusPlusRecommender.predict(int userIdx,
int itemIdx) |
protected double |
RBMRecommender.predict(int u,
int m) |
protected double |
NMFRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
LDCCRecommender.predict(int userIdx,
int itemIdx) |
protected double |
GPLSARecommender.predict(int userIdx,
int itemIdx) |
protected double |
FMSGDRecommender.predict(int userIdx,
int itemIdx)
Deprecated.
|
protected double |
FMALSRecommender.predict(int userIdx,
int itemIdx)
Deprecated.
|
protected double |
BPoissMFRecommender.predict(int userIdx,
int itemIdx) |
protected double |
BiasedMFRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
ASVDPlusPlusRecommender.predict(int userIdx,
int itemIdx) |
protected double |
AspectModelRecommender.predict(int userIdx,
int itemIdx) |
protected BPMFRecommender.HyperParameters |
BPMFRecommender.samplingHyperParameters(BPMFRecommender.HyperParameters hyperParameters,
DenseMatrix factors,
DenseVector normalMu0,
double normalBeta0,
DenseMatrix WishartScale0,
double WishartNu0) |
protected void |
URPRecommender.setup() |
protected void |
SVDPlusPlusRecommender.setup() |
protected void |
RFRecRecommender.setup() |
protected void |
RBMRecommender.setup() |
protected void |
PMFRecommender.setup() |
protected void |
NMFRecommender.setup() |
protected void |
LLORMARecommender.setup() |
protected void |
LDCCRecommender.setup() |
protected void |
GPLSARecommender.setup() |
protected void |
FMSGDRecommender.setup() |
protected void |
FMALSRecommender.setup() |
protected void |
BPoissMFRecommender.setup() |
protected void |
BPMFRecommender.setup() |
protected void |
BiasedMFRecommender.setup() |
protected void |
ASVDPlusPlusRecommender.setup() |
protected void |
AspectModelRecommender.setup() |
protected void |
SVDPlusPlusRecommender.trainModel() |
protected void |
RFRecRecommender.trainModel() |
protected void |
RBMRecommender.trainModel() |
protected void |
PMFRecommender.trainModel() |
protected void |
NMFRecommender.trainModel() |
protected void |
MFALSRecommender.trainModel() |
protected void |
LLORMARecommender.trainModel() |
protected void |
GPLSARecommender.trainModel() |
protected void |
FMSGDRecommender.trainModel() |
protected void |
FMALSRecommender.trainModel() |
protected void |
BPoissMFRecommender.trainModel() |
protected void |
BPMFRecommender.trainModel() |
protected void |
BiasedMFRecommender.trainModel() |
protected void |
ASVDPlusPlusRecommender.trainModel() |
protected void |
AspectModelRecommender.trainModel() |
protected DenseVector |
BPMFRecommender.updateParameters(DenseMatrix factors,
SparseVector ratings,
BPMFRecommender.HyperParameters hyperParameters) |
Modifier and Type | Method and Description |
---|---|
java.util.Map<Measure.MeasureValue,java.lang.Double> |
HFTRecommender.evaluateMap() |
java.util.Map<Measure.MeasureValue,java.lang.Double> |
EFMRecommender.evaluateMap() |
protected void |
HFTRecommender.setup() |
protected void |
EFMRecommender.setup() |
protected void |
EFMRecommender.trainModel() |
Modifier and Type | Method and Description |
---|---|
protected double |
SBPRRecommender.predict(int userIdx,
int itemIdx)
predict a specific ranking score for user userIdx on item itemIdx.
|
void |
SBPRRecommender.setup() |
protected void |
SBPRRecommender.trainModel() |
Modifier and Type | Method and Description |
---|---|
protected double |
TrustSVDRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
TimeSVDRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
TrustSVDRecommender.predict(int userIdx,
int itemIdx,
boolean bounded) |
protected double |
SoRegRecommender.predict(int userIdx,
int itemIdx,
boolean bound)
predict a specific rating for user userIdx on item itemIdx.
|
void |
TrustSVDRecommender.setup()
initial the model
|
void |
TrustMFRecommender.setup() |
protected void |
TimeSVDRecommender.setup() |
void |
SoRegRecommender.setup() |
void |
SoRecRecommender.setup() |
void |
SocialMFRecommender.setup() |
void |
RSTERecommender.setup() |
protected void |
TrustSVDRecommender.trainModel()
train model process
|
protected void |
TrustMFRecommender.trainModel() |
protected void |
TimeSVDRecommender.trainModel() |
protected void |
SoRegRecommender.trainModel() |
protected void |
SoRecRecommender.trainModel() |
protected void |
SocialMFRecommender.trainModel() |
protected void |
RSTERecommender.trainModel() |
protected void |
TrustMFRecommender.TrusteeMF()
Build TrusteeMF model: We*Ve
|
protected void |
TrustMFRecommender.TrusterMF()
Build TrusterMF model: Br*Vr
|
Modifier and Type | Method and Description |
---|---|
protected double |
SlopeOneRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
PersonalityDiagnosisRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
ExternalRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected double |
AssociationRuleRecommender.predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
|
protected void |
SlopeOneRecommender.setup()
initialization
|
protected void |
PRankDRecommender.setup()
initialization
|
protected void |
PersonalityDiagnosisRecommender.setup()
initialization
|
protected void |
AssociationRuleRecommender.setup()
setup
|
protected void |
SlopeOneRecommender.trainModel()
train model
|
protected void |
PRankDRecommender.trainModel()
train model
|
protected void |
PersonalityDiagnosisRecommender.trainModel()
train model
|
protected void |
ExternalRecommender.trainModel() |
protected void |
AssociationRuleRecommender.trainModel() |
Modifier and Type | Method and Description |
---|---|
protected double |
HybridRecommender.predict(int userIdx,
int itemIdx) |
protected void |
HybridRecommender.setup()
initialization
|
protected void |
HybridRecommender.trainModel()
train model
|
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