Modifier and Type | Class and Description |
---|---|
class |
NoSuchItemException |
class |
NoSuchUserException |
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 |
---|---|
V |
Cache.get(K key)
Returns cached value for a key.
|
V |
Retriever.get(K key) |
Modifier and Type | Method and Description |
---|---|
static DataSource |
AbstractJDBCComponent.lookupDataSource(String dataSourceName)
Looks up a
DataSource by name from JNDI. |
Modifier and Type | Method and Description |
---|---|
Void |
AbstractDifferenceRecommenderEvaluator.PreferenceEstimateCallable.call() |
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) |
static void |
OrderBasedRecommenderEvaluator.evaluate(Recommender recommender1,
Recommender recommender2,
int samples,
RunningAverage tracker,
String tag) |
protected static void |
AbstractDifferenceRecommenderEvaluator.execute(Collection<Callable<Void>> callables,
AtomicInteger noEstimateCounter,
RunningAverageAndStdDev timing) |
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) |
static LoadStatistics |
LoadEvaluator.runLoad(Recommender recommender) |
static LoadStatistics |
LoadEvaluator.runLoad(Recommender recommender,
int howMany) |
Modifier and Type | Method and Description |
---|---|
LongPrimitiveIterator |
PlusAnonymousUserDataModel.getItemIDs() |
FastIDSet |
PlusAnonymousUserDataModel.getItemIDsFromUser(long userID) |
FastIDSet |
GenericDataModel.getItemIDsFromUser(long userID) |
FastIDSet |
PlusAnonymousConcurrentUserDataModel.getItemIDsFromUser(long userID) |
FastIDSet |
GenericBooleanPrefDataModel.getItemIDsFromUser(long userID) |
int |
PlusAnonymousUserDataModel.getNumItems() |
int |
PlusAnonymousUserDataModel.getNumUsers() |
int |
PlusAnonymousConcurrentUserDataModel.getNumUsers() |
int |
PlusAnonymousUserDataModel.getNumUsersWithPreferenceFor(long itemID) |
int |
PlusAnonymousConcurrentUserDataModel.getNumUsersWithPreferenceFor(long itemID) |
int |
PlusAnonymousUserDataModel.getNumUsersWithPreferenceFor(long itemID1,
long itemID2) |
int |
PlusAnonymousConcurrentUserDataModel.getNumUsersWithPreferenceFor(long itemID1,
long itemID2) |
PreferenceArray |
PlusAnonymousUserDataModel.getPreferencesForItem(long itemID) |
PreferenceArray |
PlusAnonymousConcurrentUserDataModel.getPreferencesForItem(long itemID) |
PreferenceArray |
PlusAnonymousUserDataModel.getPreferencesFromUser(long userID) |
PreferenceArray |
PlusAnonymousConcurrentUserDataModel.getPreferencesFromUser(long userID) |
Long |
PlusAnonymousUserDataModel.getPreferenceTime(long userID,
long itemID) |
Long |
GenericDataModel.getPreferenceTime(long userID,
long itemID) |
Long |
PlusAnonymousConcurrentUserDataModel.getPreferenceTime(long userID,
long itemID) |
Long |
GenericBooleanPrefDataModel.getPreferenceTime(long userID,
long itemID) |
Float |
PlusAnonymousUserDataModel.getPreferenceValue(long userID,
long itemID) |
Float |
GenericDataModel.getPreferenceValue(long userID,
long itemID) |
Float |
PlusAnonymousConcurrentUserDataModel.getPreferenceValue(long userID,
long itemID) |
LongPrimitiveIterator |
PlusAnonymousUserDataModel.getUserIDs() |
LongPrimitiveIterator |
PlusAnonymousConcurrentUserDataModel.getUserIDs() |
void |
AbstractJDBCIDMigrator.initialize(Iterable<String> stringIDs) |
void |
PlusAnonymousUserDataModel.removePreference(long userID,
long itemID) |
void |
PlusAnonymousConcurrentUserDataModel.removePreference(long userID,
long itemID) |
void |
PlusAnonymousUserDataModel.setPreference(long userID,
long itemID,
float value) |
void |
PlusAnonymousConcurrentUserDataModel.setPreference(long userID,
long itemID,
float value) |
void |
AbstractJDBCIDMigrator.storeMapping(long longID,
String stringID) |
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.
|
String |
AbstractJDBCIDMigrator.toStringID(long longID) |
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) |
Modifier and Type | Method and Description |
---|---|
LongPrimitiveIterator |
FileDataModel.getItemIDs() |
FastIDSet |
FileDataModel.getItemIDsFromUser(long userID) |
int |
FileDataModel.getNumItems() |
int |
FileDataModel.getNumUsers() |
int |
FileDataModel.getNumUsersWithPreferenceFor(long itemID) |
int |
FileDataModel.getNumUsersWithPreferenceFor(long itemID1,
long itemID2) |
PreferenceArray |
FileDataModel.getPreferencesForItem(long itemID) |
PreferenceArray |
FileDataModel.getPreferencesFromUser(long userID) |
Long |
FileDataModel.getPreferenceTime(long userID,
long itemID) |
Float |
FileDataModel.getPreferenceValue(long userID,
long itemID) |
LongPrimitiveIterator |
FileDataModel.getUserIDs() |
void |
FileDataModel.removePreference(long userID,
long itemID)
See the warning at
FileDataModel.setPreference(long, long, float) . |
void |
FileDataModel.setPreference(long userID,
long itemID,
float value)
Note that this method only updates the in-memory preference data that this
FileDataModel
maintains; it does not modify any data on disk. |
Modifier and Type | Method and Description |
---|---|
long[] |
CachingUserNeighborhood.getUserNeighborhood(long userID) |
long[] |
ThresholdUserNeighborhood.getUserNeighborhood(long userID) |
long[] |
NearestNUserNeighborhood.getUserNeighborhood(long userID) |
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) |
Modifier and Type | Method and Description |
---|---|
protected float |
GenericUserBasedRecommender.doEstimatePreference(long theUserID,
long[] theNeighborhood,
long itemID) |
protected float |
GenericBooleanPrefUserBasedRecommender.doEstimatePreference(long theUserID,
long[] theNeighborhood,
long itemID)
This computation is in a technical sense, wrong, since in the domain of "boolean preference users" where
all preference values are 1, this method should only ever return 1.0 or NaN.
|
protected float |
GenericItemBasedRecommender.doEstimatePreference(long userID,
PreferenceArray preferencesFromUser,
long itemID) |
protected float |
GenericBooleanPrefItemBasedRecommender.doEstimatePreference(long userID,
PreferenceArray preferencesFromUser,
long itemID)
This computation is in a technical sense, wrong, since in the domain of "boolean preference users" where
all preference values are 1, this method should only ever return 1.0 or NaN.
|
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
|
double |
GenericItemBasedRecommender.MostSimilarEstimator.estimate(Long itemID) |
double |
TopItems.Estimator.estimate(T thing) |
float |
GenericUserBasedRecommender.estimatePreference(long userID,
long itemID) |
float |
GenericItemBasedRecommender.estimatePreference(long userID,
long itemID) |
float |
CachingRecommender.estimatePreference(long userID,
long itemID) |
float |
ItemAverageRecommender.estimatePreference(long userID,
long itemID) |
float |
ItemUserAverageRecommender.estimatePreference(long userID,
long itemID) |
protected FastIDSet |
GenericUserBasedRecommender.getAllOtherItems(long[] theNeighborhood,
long theUserID,
boolean includeKnownItems) |
protected FastIDSet |
GenericBooleanPrefUserBasedRecommender.getAllOtherItems(long[] theNeighborhood,
long theUserID,
boolean includeKnownItems) |
protected FastIDSet |
AbstractRecommender.getAllOtherItems(long userID,
PreferenceArray preferencesFromUser,
boolean includeKnownItems) |
FastIDSet |
AbstractCandidateItemsStrategy.getCandidateItems(long[] itemIDs,
DataModel dataModel) |
FastIDSet |
AbstractCandidateItemsStrategy.getCandidateItems(long userID,
PreferenceArray preferencesFromUser,
DataModel dataModel,
boolean includeKnownItems) |
static List<RecommendedItem> |
TopItems.getTopItems(int howMany,
LongPrimitiveIterator possibleItemIDs,
IDRescorer rescorer,
TopItems.Estimator<Long> estimator) |
static long[] |
TopItems.getTopUsers(int howMany,
LongPrimitiveIterator allUserIDs,
IDRescorer rescorer,
TopItems.Estimator<Long> estimator) |
List<RecommendedItem> |
GenericItemBasedRecommender.mostSimilarItems(long[] itemIDs,
int howMany) |
List<RecommendedItem> |
GenericItemBasedRecommender.mostSimilarItems(long[] itemIDs,
int howMany,
boolean excludeItemIfNotSimilarToAll) |
List<RecommendedItem> |
GenericItemBasedRecommender.mostSimilarItems(long[] itemIDs,
int howMany,
Rescorer<LongPair> rescorer) |
List<RecommendedItem> |
GenericItemBasedRecommender.mostSimilarItems(long[] itemIDs,
int howMany,
Rescorer<LongPair> rescorer,
boolean excludeItemIfNotSimilarToAll) |
List<RecommendedItem> |
GenericItemBasedRecommender.mostSimilarItems(long itemID,
int howMany) |
List<RecommendedItem> |
GenericItemBasedRecommender.mostSimilarItems(long itemID,
int howMany,
Rescorer<LongPair> rescorer) |
long[] |
GenericUserBasedRecommender.mostSimilarUserIDs(long userID,
int howMany) |
long[] |
GenericUserBasedRecommender.mostSimilarUserIDs(long userID,
int howMany,
Rescorer<LongPair> rescorer) |
List<RecommendedItem> |
CachingRecommender.recommend(long userID,
int howMany) |
List<RecommendedItem> |
AbstractRecommender.recommend(long userID,
int howMany)
Default implementation which just calls
Recommender.recommend(long, int, org.apache.mahout.cf.taste.recommender.IDRescorer) , with a
Rescorer that does nothing. |
List<RecommendedItem> |
CachingRecommender.recommend(long userID,
int howMany,
boolean includeKnownItems) |
List<RecommendedItem> |
AbstractRecommender.recommend(long userID,
int howMany,
boolean includeKnownItems)
Default implementation which just calls
Recommender.recommend(long, int, org.apache.mahout.cf.taste.recommender.IDRescorer) , with a
Rescorer that does nothing. |
List<RecommendedItem> |
CachingRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer) |
List<RecommendedItem> |
AbstractRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer)
|
List<RecommendedItem> |
GenericUserBasedRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer,
boolean includeKnownItems) |
List<RecommendedItem> |
GenericItemBasedRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer,
boolean includeKnownItems) |
List<RecommendedItem> |
CachingRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer,
boolean includeKnownItems) |
List<RecommendedItem> |
ItemAverageRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer,
boolean includeKnownItems) |
List<RecommendedItem> |
RandomRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer,
boolean includeKnownItems) |
List<RecommendedItem> |
ItemUserAverageRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer,
boolean includeKnownItems) |
List<RecommendedItem> |
GenericItemBasedRecommender.recommendedBecause(long userID,
long itemID,
int howMany) |
void |
CachingRecommender.removePreference(long userID,
long itemID) |
void |
ItemAverageRecommender.removePreference(long userID,
long itemID) |
void |
ItemUserAverageRecommender.removePreference(long userID,
long itemID) |
void |
AbstractRecommender.removePreference(long userID,
long itemID)
Default implementation which just calls
DataModel.removePreference(long, long) (Object, Object)}. |
void |
CachingRecommender.setPreference(long userID,
long itemID,
float value) |
void |
ItemAverageRecommender.setPreference(long userID,
long itemID,
float value) |
void |
ItemUserAverageRecommender.setPreference(long userID,
long itemID,
float value) |
void |
AbstractRecommender.setPreference(long userID,
long itemID,
float value)
Default implementation which just calls
DataModel.setPreference(long, long, float) . |
Constructor and Description |
---|
CachingRecommender(Recommender recommender) |
ItemAverageRecommender(DataModel dataModel) |
ItemUserAverageRecommender(DataModel dataModel) |
RandomRecommender(DataModel dataModel) |
Modifier and Type | Method and Description |
---|---|
float |
SVDRecommender.estimatePreference(long userID,
long itemID)
a preference is estimated by computing the dot-product of the user and item feature vectors
|
Factorization |
SVDPlusPlusFactorizer.factorize() |
Factorization |
RatingSGDFactorizer.factorize() |
Factorization |
Factorizer.factorize() |
Factorization |
ParallelSGDFactorizer.factorize() |
Factorization |
ALSWRFactorizer.factorize() |
protected void |
ParallelSGDFactorizer.initialize() |
protected void |
SVDPlusPlusFactorizer.prepareTraining() |
protected void |
RatingSGDFactorizer.prepareTraining() |
List<RecommendedItem> |
SVDRecommender.recommend(long userID,
int howMany,
IDRescorer rescorer,
boolean includeKnownItems) |
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 |
---|---|
long[] |
AbstractItemSimilarity.allSimilarItemIDs(long itemID) |
long[] |
CachingItemSimilarity.allSimilarItemIDs(long itemID) |
float |
AveragingPreferenceInferrer.inferPreference(long userID,
long itemID) |
double[] |
LogLikelihoodSimilarity.itemSimilarities(long itemID1,
long[] itemID2s) |
double[] |
CityBlockSimilarity.itemSimilarities(long itemID1,
long[] itemID2s) |
double[] |
TanimotoCoefficientSimilarity.itemSimilarities(long itemID1,
long[] itemID2s) |
double[] |
CachingItemSimilarity.itemSimilarities(long itemID1,
long[] itemID2s) |
double |
LogLikelihoodSimilarity.itemSimilarity(long itemID1,
long itemID2) |
double |
CityBlockSimilarity.itemSimilarity(long itemID1,
long itemID2) |
double |
TanimotoCoefficientSimilarity.itemSimilarity(long itemID1,
long itemID2) |
double |
CachingItemSimilarity.itemSimilarity(long itemID1,
long itemID2) |
double |
LogLikelihoodSimilarity.userSimilarity(long userID1,
long userID2) |
double |
CityBlockSimilarity.userSimilarity(long userID1,
long userID2) |
double |
SpearmanCorrelationSimilarity.userSimilarity(long userID1,
long userID2) |
double |
TanimotoCoefficientSimilarity.userSimilarity(long userID1,
long userID2) |
double |
CachingUserSimilarity.userSimilarity(long userID1,
long userID2) |
Modifier and Type | Method and Description |
---|---|
long[] |
FileItemSimilarity.allSimilarItemIDs(long itemID) |
double[] |
FileItemSimilarity.itemSimilarities(long itemID1,
long[] itemID2s) |
double |
FileItemSimilarity.itemSimilarity(long itemID1,
long itemID2) |
Modifier and Type | Method and Description |
---|---|
FastByIDMap<FastIDSet> |
JDBCDataModel.exportWithIDsOnly() |
FastByIDMap<PreferenceArray> |
JDBCDataModel.exportWithPrefs()
Hmm, should this exist elsewhere? seems like most relevant for a DB implementation, which is not in
memory, which might want to export to memory.
|
LongPrimitiveIterator |
DataModel.getItemIDs() |
FastIDSet |
DataModel.getItemIDsFromUser(long userID) |
int |
DataModel.getNumItems() |
int |
DataModel.getNumUsers() |
int |
DataModel.getNumUsersWithPreferenceFor(long itemID) |
int |
DataModel.getNumUsersWithPreferenceFor(long itemID1,
long itemID2) |
PreferenceArray |
DataModel.getPreferencesForItem(long itemID) |
PreferenceArray |
DataModel.getPreferencesFromUser(long userID) |
Long |
DataModel.getPreferenceTime(long userID,
long itemID)
Retrieves the time at which a preference value from a user and item was set, if known.
|
Float |
DataModel.getPreferenceValue(long userID,
long itemID)
Retrieves the preference value for a single user and item.
|
LongPrimitiveIterator |
DataModel.getUserIDs() |
void |
UpdatableIDMigrator.initialize(Iterable<String> stringIDs)
Make the mapping aware of the given string IDs.
|
void |
DataModel.removePreference(long userID,
long itemID)
Removes a particular preference for a user.
|
void |
DataModel.setPreference(long userID,
long itemID,
float value)
Sets a particular preference (item plus rating) for a user.
|
void |
UpdatableIDMigrator.storeMapping(long longID,
String stringID)
Stores the reverse long-to-String mapping in some kind of backing store.
|
String |
IDMigrator.toStringID(long longID) |
Modifier and Type | Method and Description |
---|---|
long[] |
UserNeighborhood.getUserNeighborhood(long userID) |
Modifier and Type | Method and Description |
---|---|
float |
Recommender.estimatePreference(long userID,
long itemID) |
FastIDSet |
MostSimilarItemsCandidateItemsStrategy.getCandidateItems(long[] itemIDs,
DataModel dataModel) |
FastIDSet |
CandidateItemsStrategy.getCandidateItems(long userID,
PreferenceArray preferencesFromUser,
DataModel dataModel,
boolean includeKnownItems) |
List<RecommendedItem> |
ItemBasedRecommender.mostSimilarItems(long[] itemIDs,
int howMany) |
List<RecommendedItem> |
ItemBasedRecommender.mostSimilarItems(long[] itemIDs,
int howMany,
boolean excludeItemIfNotSimilarToAll) |
List<RecommendedItem> |
ItemBasedRecommender.mostSimilarItems(long[] itemIDs,
int howMany,
Rescorer<LongPair> rescorer) |
List<RecommendedItem> |
ItemBasedRecommender.mostSimilarItems(long[] itemIDs,
int howMany,
Rescorer<LongPair> rescorer,
boolean excludeItemIfNotSimilarToAll) |
List<RecommendedItem> |
ItemBasedRecommender.mostSimilarItems(long itemID,
int howMany) |
List<RecommendedItem> |
ItemBasedRecommender.mostSimilarItems(long itemID,
int howMany,
Rescorer<LongPair> rescorer) |
long[] |
UserBasedRecommender.mostSimilarUserIDs(long userID,
int howMany) |
long[] |
UserBasedRecommender.mostSimilarUserIDs(long userID,
int howMany,
Rescorer<LongPair> rescorer) |
List<RecommendedItem> |
Recommender.recommend(long userID,
int howMany) |
List<RecommendedItem> |
Recommender.recommend(long userID,
int howMany,
boolean includeKnownItems) |
List<RecommendedItem> |
Recommender.recommend(long userID,
int howMany,
IDRescorer rescorer) |
List<RecommendedItem> |
Recommender.recommend(long userID,
int howMany,
IDRescorer rescorer,
boolean includeKnownItems) |
List<RecommendedItem> |
ItemBasedRecommender.recommendedBecause(long userID,
long itemID,
int howMany)
Lists the items that were most influential in recommending a given item to a given user.
|
void |
Recommender.removePreference(long userID,
long itemID) |
void |
Recommender.setPreference(long userID,
long itemID,
float value) |
Modifier and Type | Method and Description |
---|---|
long[] |
ItemSimilarity.allSimilarItemIDs(long itemID) |
float |
PreferenceInferrer.inferPreference(long userID,
long itemID)
Infers the given user's preference value for an item.
|
double[] |
ItemSimilarity.itemSimilarities(long itemID1,
long[] itemID2s)
A bulk-get version of
ItemSimilarity.itemSimilarity(long, long) . |
double |
ItemSimilarity.itemSimilarity(long itemID1,
long itemID2)
Returns the degree of similarity, of two items, based on the preferences that users have expressed for
the items.
|
double |
UserSimilarity.userSimilarity(long userID1,
long userID2)
Returns the degree of similarity, of two users, based on the their preferences.
|
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