Modifier and Type | Method and Description |
---|---|
DataModel |
DataModelBuilder.buildDataModel(FastByIDMap<PreferenceArray> trainingData)
Builds a
DataModel implementation to be used in an evaluation, given training data. |
Modifier and Type | Method and Description |
---|---|
Recommender |
RecommenderBuilder.buildRecommender(DataModel dataModel)
Builds a
Recommender implementation to be evaluated, using the given DataModel . |
double |
RecommenderEvaluator.evaluate(RecommenderBuilder recommenderBuilder,
DataModelBuilder dataModelBuilder,
DataModel dataModel,
double trainingPercentage,
double evaluationPercentage)
Evaluates the quality of a
Recommender 's recommendations. |
IRStatistics |
RecommenderIRStatsEvaluator.evaluate(RecommenderBuilder recommenderBuilder,
DataModelBuilder dataModelBuilder,
DataModel dataModel,
IDRescorer rescorer,
int at,
double relevanceThreshold,
double evaluationPercentage) |
FastIDSet |
RelevantItemsDataSplitter.getRelevantItemsIDs(long userID,
int at,
double relevanceThreshold,
DataModel dataModel)
During testing, relevant items are removed from a particular users' preferences,
and a model is build using this user's other preferences and all other users.
|
void |
RelevantItemsDataSplitter.processOtherUser(long userID,
FastIDSet relevantItemIDs,
FastByIDMap<PreferenceArray> trainingUsers,
long otherUserID,
DataModel dataModel)
Adds a single user and all their preferences to the training model.
|
Modifier and Type | Method and Description |
---|---|
static void |
OrderBasedRecommenderEvaluator.evaluate(DataModel model1,
DataModel model2,
int samples,
RunningAverage tracker,
String tag) |
double |
AbstractDifferenceRecommenderEvaluator.evaluate(RecommenderBuilder recommenderBuilder,
DataModelBuilder dataModelBuilder,
DataModel dataModel,
double trainingPercentage,
double evaluationPercentage) |
IRStatistics |
GenericRecommenderIRStatsEvaluator.evaluate(RecommenderBuilder recommenderBuilder,
DataModelBuilder dataModelBuilder,
DataModel dataModel,
IDRescorer rescorer,
int at,
double relevanceThreshold,
double evaluationPercentage) |
static void |
OrderBasedRecommenderEvaluator.evaluate(Recommender recommender,
DataModel model,
int samples,
RunningAverage tracker,
String tag) |
FastIDSet |
GenericRelevantItemsDataSplitter.getRelevantItemsIDs(long userID,
int at,
double relevanceThreshold,
DataModel dataModel) |
void |
GenericRelevantItemsDataSplitter.processOtherUser(long userID,
FastIDSet relevantItemIDs,
FastByIDMap<PreferenceArray> trainingUsers,
long otherUserID,
DataModel dataModel) |
Modifier and Type | Class and Description |
---|---|
class |
AbstractDataModel
Contains some features common to all implementations.
|
class |
GenericBooleanPrefDataModel
A simple
DataModel which uses given user data as its data source. |
class |
GenericDataModel
|
class |
PlusAnonymousConcurrentUserDataModel
This is a special thread-safe version of
PlusAnonymousUserDataModel
which allow multiple concurrent anonymous requests. |
class |
PlusAnonymousUserDataModel
|
Modifier and Type | Method and Description |
---|---|
protected DataModel |
PlusAnonymousUserDataModel.getDelegate() |
Modifier and Type | Method and Description |
---|---|
static FastByIDMap<PreferenceArray> |
GenericDataModel.toDataMap(DataModel dataModel)
Exports the simple user IDs and preferences in the data model.
|
static FastByIDMap<FastIDSet> |
GenericBooleanPrefDataModel.toDataMap(DataModel dataModel)
Exports the simple user IDs and associated item IDs in the data model.
|
Constructor and Description |
---|
GenericBooleanPrefDataModel(DataModel dataModel)
Deprecated.
without direct replacement.
Consider
GenericBooleanPrefDataModel.toDataMap(DataModel) with GenericBooleanPrefDataModel.GenericBooleanPrefDataModel(FastByIDMap) |
GenericDataModel(DataModel dataModel)
Deprecated.
without direct replacement.
Consider
GenericDataModel.toDataMap(DataModel) with GenericDataModel.GenericDataModel(FastByIDMap) |
PlusAnonymousConcurrentUserDataModel(DataModel delegate,
int maxConcurrentUsers) |
PlusAnonymousUserDataModel(DataModel delegate) |
Modifier and Type | Class and Description |
---|---|
class |
FileDataModel
A
DataModel backed by a delimited file. |
Modifier and Type | Method and Description |
---|---|
protected DataModel |
FileDataModel.buildModel() |
Constructor and Description |
---|
CachingUserNeighborhood(UserNeighborhood neighborhood,
DataModel dataModel) |
NearestNUserNeighborhood(int n,
double minSimilarity,
UserSimilarity userSimilarity,
DataModel dataModel) |
NearestNUserNeighborhood(int n,
double minSimilarity,
UserSimilarity userSimilarity,
DataModel dataModel,
double samplingRate) |
NearestNUserNeighborhood(int n,
UserSimilarity userSimilarity,
DataModel dataModel) |
ThresholdUserNeighborhood(double threshold,
UserSimilarity userSimilarity,
DataModel dataModel) |
ThresholdUserNeighborhood(double threshold,
UserSimilarity userSimilarity,
DataModel dataModel,
double samplingRate) |
Modifier and Type | Method and Description |
---|---|
DataModel |
CachingRecommender.getDataModel() |
DataModel |
AbstractRecommender.getDataModel() |
Modifier and Type | Method and Description |
---|---|
protected FastIDSet |
AbstractCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs,
DataModel dataModel) |
protected FastIDSet |
SamplingCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs,
DataModel dataModel,
boolean includeKnownItems) |
protected FastIDSet |
AllUnknownItemsCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs,
DataModel dataModel,
boolean includeKnownItems)
return all items the user has not yet seen
|
protected FastIDSet |
AllSimilarItemsCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs,
DataModel dataModel,
boolean includeKnownItems) |
protected abstract FastIDSet |
AbstractCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs,
DataModel dataModel,
boolean includeKnownItems) |
protected FastIDSet |
PreferredItemsNeighborhoodCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs,
DataModel dataModel,
boolean includeKnownItems)
returns all items that have not been rated by the user and that were preferred by another user
that has preferred at least one item that the current user has preferred too
|
FastIDSet |
AbstractCandidateItemsStrategy.getCandidateItems(long[] itemIDs,
DataModel dataModel) |
FastIDSet |
AbstractCandidateItemsStrategy.getCandidateItems(long userID,
PreferenceArray preferencesFromUser,
DataModel dataModel,
boolean includeKnownItems) |
Modifier and Type | Field and Description |
---|---|
protected DataModel |
RatingSGDFactorizer.dataModel |
Constructor and Description |
---|
AbstractFactorizer(DataModel dataModel) |
ALSWRFactorizer(DataModel dataModel,
int numFeatures,
double lambda,
int numIterations) |
ALSWRFactorizer(DataModel dataModel,
int numFeatures,
double lambda,
int numIterations,
boolean usesImplicitFeedback,
double alpha) |
ALSWRFactorizer(DataModel dataModel,
int numFeatures,
double lambda,
int numIterations,
boolean usesImplicitFeedback,
double alpha,
int numTrainingThreads) |
ParallelSGDFactorizer(DataModel dataModel,
int numFeatures,
double lambda,
int numEpochs) |
ParallelSGDFactorizer(DataModel dataModel,
int numFeatures,
double lambda,
int numIterations,
double mu0,
double decayFactor,
int stepOffset,
double forgettingExponent) |
ParallelSGDFactorizer(DataModel dataModel,
int numFeatures,
double lambda,
int numIterations,
double mu0,
double decayFactor,
int stepOffset,
double forgettingExponent,
double biasMuRatio,
double biasLambdaRatio) |
ParallelSGDFactorizer(DataModel dataModel,
int numFeatures,
double lambda,
int numIterations,
double mu0,
double decayFactor,
int stepOffset,
double forgettingExponent,
double biasMuRatio,
double biasLambdaRatio,
int numThreads) |
ParallelSGDFactorizer(DataModel dataModel,
int numFeatures,
double lambda,
int numIterations,
double mu0,
double decayFactor,
int stepOffset,
double forgettingExponent,
int numThreads) |
PreferenceShuffler(DataModel dataModel) |
RatingSGDFactorizer(DataModel dataModel,
int numFeatures,
double learningRate,
double preventOverfitting,
double randomNoise,
int numIterations,
double learningRateDecay) |
RatingSGDFactorizer(DataModel dataModel,
int numFeatures,
int numIterations) |
SVDPlusPlusFactorizer(DataModel dataModel,
int numFeatures,
double learningRate,
double preventOverfitting,
double randomNoise,
int numIterations,
double learningRateDecay) |
SVDPlusPlusFactorizer(DataModel dataModel,
int numFeatures,
int numIterations) |
SVDRecommender(DataModel dataModel,
Factorizer factorizer) |
SVDRecommender(DataModel dataModel,
Factorizer factorizer,
CandidateItemsStrategy candidateItemsStrategy) |
SVDRecommender(DataModel dataModel,
Factorizer factorizer,
CandidateItemsStrategy candidateItemsStrategy,
PersistenceStrategy persistenceStrategy)
Create an SVDRecommender using a persistent store to cache factorizations.
|
SVDRecommender(DataModel dataModel,
Factorizer factorizer,
PersistenceStrategy persistenceStrategy)
Create an SVDRecommender using a persistent store to cache factorizations.
|
Modifier and Type | Method and Description |
---|---|
protected DataModel |
AbstractItemSimilarity.getDataModel() |
Modifier and Type | Interface and Description |
---|---|
interface |
JDBCDataModel |
Modifier and Type | Method and Description |
---|---|
DataModel |
Recommender.getDataModel() |
Modifier and Type | Method and Description |
---|---|
FastIDSet |
MostSimilarItemsCandidateItemsStrategy.getCandidateItems(long[] itemIDs,
DataModel dataModel) |
FastIDSet |
CandidateItemsStrategy.getCandidateItems(long userID,
PreferenceArray preferencesFromUser,
DataModel dataModel,
boolean includeKnownItems) |
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