Class Hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 12345678]

CAttributeData	Class that offers static methods to read (binary) attribute data into IBooleanMatrix objects
CAUC	Area under the ROC curve (AUC) of a list of ranked items
CConstants	Static class containing constants used by the MyMediaLite Input/Output routines
CDataReaderExtensions	Extension methods for IDataReader objects
►CDictionary
►CEvaluationResults	Class for representing evaluation results
CItemRecommendationEvaluationResults	Item recommendation evaluation results
CRatingPredictionEvaluationResults	Rating prediction evaluation results
CRecommenderParameters	Class for key-value pair string processing
CEntityMappingExtensions	I/O routines for classes implementing the IEntityMapping interface
CExtensions	Extension methods for correlation matrices
CExtensions	Class that contains static methods for rating prediction
CExtensions	Extension methods for dataset statistics
CExtensions	Helper class with utility methods for handling recommenders
CExtensions	Class that contains static methods for item prediction
CFileSerializer	Static class for serializing objects to binary files
CFileSystem	File-system related helper functions
CFoldIn	Fold-in evaluation
CFoldInRatingPredictorExtensions	Extension methods for IFoldInRatingPredictor
CGridSearch	Grid search for finding suitable hyperparameters
CHandlers	Class containing handler functions, e.g. exception handlers
►CICloneable
►CIRecommender	Generic interface for simple recommenders
►CEnsemble	Abtract class for combining several prediction methods
CWeightedEnsemble	Combining several predictors with a weighted ensemble
►CIItemAttributeAwareRecommender	Interface for recommenders that take binary item attributes into account
CItemAttributeKNN	k-nearest neighbor (kNN) item-based collaborative filtering using the correlation of the item attibutes
CMostPopularByAttributes	Recommend most popular items by attribute
CGSVDPlusPlus	Item Attribute Aware SVD++: Matrix factorization that also takes into account what users have rated and its attributes.
CItemAttributeKNN	Attribute-aware weighted item-based kNN recommender
CNaiveBayes	Attribute-aware rating predictor using Naive Bayes
CIItemRelationAwareRecommender	Interface for recommenders that take a binary relation over items into account
►CIFoldInItemRecommender	Item recommender that allows folding in new users
►CBPRMF	Matrix factorization model for item prediction (ranking) optimized for BPR
CMultiCoreBPRMF	Matrix factorization for BPR on multiple cores
CSoftMarginRankingMF	Matrix factorization model for item prediction optimized for a soft margin (hinge) ranking loss, using stochastic gradient descent (as in BPR-MF).
CWeightedBPRMF	Weigthed BPR-MF with frequency-adjusted sampling
►CUserKNN	k-nearest neighbor user-based collaborative filtering
CUserAttributeKNN	k-nearest neighbor (kNN) user-based collaborative filtering using the correlation of the user attibutes
►CIUserAttributeAwareRecommender	Interface for recommenderss that take binary user attributes into account
CUserAttributeKNN	k-nearest neighbor (kNN) user-based collaborative filtering using the correlation of the user attibutes
CUserAttributeKNN	Weighted kNN recommender based on user attributes
►CIUserRelationAwareRecommender	Interface for recommenders that take a binary relation over users into account
CSocialMF	Social-network-aware matrix factorization
►CIRatingPredictor	Interface for rating predictors
►CIFoldInRatingPredictor	Rating predictor that allows folding in new users
►CItemKNN	Weighted item-based kNN
CItemAttributeKNN	Attribute-aware weighted item-based kNN recommender
►CMatrixFactorization	Simple matrix factorization class, learning is performed by stochastic gradient descent (SGD)
►CBiasedMatrixFactorization	Matrix factorization with explicit user and item bias, learning is performed by stochastic gradient descent
CSigmoidCombinedAsymmetricFactorModel	Asymmetric factor model which represents items in terms of the users that rated them, and users in terms of the items they rated
CSigmoidItemAsymmetricFactorModel	Asymmetric factor model
CSigmoidUserAsymmetricFactorModel	Asymmetric factor model which represents items in terms of the users that rated them
CSocialMF	Social-network-aware matrix factorization
►CSVDPlusPlus	SVD++: Matrix factorization that also takes into account what users have rated
CGSVDPlusPlus	Item Attribute Aware SVD++: Matrix factorization that also takes into account what users have rated and its attributes.
CSigmoidSVDPlusPlus	SVD++: Matrix factorization that also takes into account what users have rated; variant that uses a sigmoid function
CUserAverage	Uses the average rating value of a user for predictions
►CUserKNN	Weighted user-based kNN
CUserAttributeKNN	Weighted kNN recommender based on user attributes
►CIIncrementalRatingPredictor	Interface for rating predictors which support incremental training
►CIncrementalRatingPredictor	Base class for rating predictors that support incremental training
CConstant	Uses a constant rating value for prediction
►CEntityAverage	Abstract class that uses an average (by entity) rating value for predictions
CItemAverage	Uses the average rating value of an item for prediction
CUserAverage	Uses the average rating value of a user for predictions
CGlobalAverage	Uses the average rating value over all ratings for prediction
►CKNN	Base class for rating predictors that use some kind of kNN
CItemKNN	Weighted item-based kNN
CUserKNN	Weighted user-based kNN
CMatrixFactorization	Simple matrix factorization class, learning is performed by stochastic gradient descent (SGD)
CNaiveBayes	Attribute-aware rating predictor using Naive Bayes
CRandom	Uses a random rating value for prediction
CUserItemBaseline	Baseline method for rating prediction
►CITimeAwareRatingPredictor	Interface for time-aware rating predictors
►CTimeAwareRatingPredictor	Abstract class for time-aware rating predictors
►CTimeAwareBaseline	Time-aware bias model
CTimeAwareBaselineWithFrequencies	Time-aware bias model with frequencies
►CITransductiveRatingPredictor	Rating predictor that knows beforehand what it will have to rate
CSigmoidCombinedAsymmetricFactorModel	Asymmetric factor model which represents items in terms of the users that rated them, and users in terms of the items they rated
CSigmoidItemAsymmetricFactorModel	Asymmetric factor model
CSigmoidSVDPlusPlus	SVD++: Matrix factorization that also takes into account what users have rated; variant that uses a sigmoid function
CSigmoidUserAsymmetricFactorModel	Asymmetric factor model which represents items in terms of the users that rated them
CSVDPlusPlus	SVD++: Matrix factorization that also takes into account what users have rated
►CRatingPredictor	Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction)
CBiPolarSlopeOne	Bi-polar frequency-weighted Slope-One rating prediction
CCoClustering	Co-clustering for rating prediction
CExternalRatingPredictor	Uses externally computed predictions
CFactorWiseMatrixFactorization	Matrix factorization with factor-wise learning
CIncrementalRatingPredictor	Base class for rating predictors that support incremental training
CLatentFeatureLogLinearModel	Latent-feature log linear model
CSlopeOne	Frequency-weighted Slope-One rating prediction
CTimeAwareRatingPredictor	Abstract class for time-aware rating predictors
►CRecommender	Abstract recommender class implementing default behaviors
►CItemRecommender	Abstract item recommender class that loads the (positive-only implicit feedback) training data into memory and provides flexible access to it.
CExternalItemRecommender	Uses externally computed predictions
►CIncrementalItemRecommender	Base class for item recommenders that support incremental updates
►CKNN	Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model
►CItemKNN	k-nearest neighbor (kNN) item-based collaborative filtering
CItemAttributeKNN	k-nearest neighbor (kNN) item-based collaborative filtering using the correlation of the item attibutes
CUserKNN	k-nearest neighbor user-based collaborative filtering
►CMF	Abstract class for matrix factorization based item predictors
CBPRMF	Matrix factorization model for item prediction (ranking) optimized for BPR
CWRMF	Weighted matrix factorization method proposed by Hu et al. and Pan et al.
CMostPopular	Most-popular item recommender
►CSLIM	Abstract class for SLIM based item predictors proposed by Ning and Karypis
CBPRSLIM	Sparse Linear Methods (SLIM) for item prediction (ranking) optimized for BPR-Opt optimization criterion
CLeastSquareSLIM	Sparse Linear Methods (SLIM) for item prediction (ranking) optimized for the elastic net loss
CMostPopularByAttributes	Recommend most popular items by attribute
CRandom	Random item recommender for use as experimental baseline
CZero	Constant item recommender for use as experimental baseline. Always predicts a score of zero
CRatingPredictor	Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction)
►CIDataSet	Interface for different kinds of collaborative filtering data sets
►CDataSet	Abstract dataset class that implements some common functions
CPosOnlyFeedback< T >	Data structure for implicit, positive-only user feedback
►CRatings	Data structure for storing ratings
CCombinedRatings	Combine two IRatings objects
CRatingsProxy	Data structure that allows access to selected entries of a rating data structure
►CStaticRatings	Array-based storage for rating data.
CStaticByteRatings	Array-based storage for rating data.
►CTimedRatings	Data structure for storing ratings with time information
CTimedRatingsProxy	Data structure that allows access to selected entries of a timed rating data structure
►CIPosOnlyFeedback	Interface for implicit, positive-only user feedback
CPosOnlyFeedback< T >	Data structure for implicit, positive-only user feedback
►CIRatings	Interface for rating datasets
►CITimedRatings	Interface for rating datasets with time information
CTimedRatings	Data structure for storing ratings with time information
CRatings	Data structure for storing ratings
►CITimedDataSet	interface for data sets with time information
CITimedRatings	Interface for rating datasets with time information
CIHyperParameterSearch	Interface for classes that perform hyper-parameter search
►CIIncrementalRecommender	Interface for recommenders that support incremental model updates.
►CIIncrementalItemRecommender	Interface for item recommenders
CIncrementalItemRecommender	Base class for item recommenders that support incremental updates
CIIncrementalRatingPredictor	Interface for rating predictors which support incremental training
►CIItemSimilarityProvider	Interface for classes that provide item similarities
CItemKNN	k-nearest neighbor (kNN) item-based collaborative filtering
CItemKNN	Weighted item-based kNN
►CIIterativeModel	Interface representing iteratively trained models
CMF	Abstract class for matrix factorization based item predictors
CSLIM	Abstract class for SLIM based item predictors proposed by Ning and Karypis
CCoClustering	Co-clustering for rating prediction
CFactorWiseMatrixFactorization	Matrix factorization with factor-wise learning
CLatentFeatureLogLinearModel	Latent-feature log linear model
CMatrixFactorization	Simple matrix factorization class, learning is performed by stochastic gradient descent (SGD)
CTimeAwareBaseline	Time-aware bias model
CUserItemBaseline	Baseline method for rating prediction
►CIList
CIRatings	Interface for rating datasets
CCombinedList< T >	Combines two List objects
CListProxy< T >	Proxy class that allows access to selected elements of an underlying list data structure
►CIMapping	Interface to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
CIdentityMapping	Identity mapping for entity IDs: Every original ID is mapped to itself
CMapping	Class to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
►CIMatrix< T >	Generic interface for matrix data types
CMatrix< T >	Class for storing dense matrices
►CSparseMatrix< T >	Class for storing sparse matrices
CSymmetricSparseMatrix< T >	a symmetric sparse matrix; consumes less memory
CSymmetricMatrix< T >	Class for storing dense matrices
►CIMatrix< bool >
►CIBooleanMatrix	Interface for boolean matrices
CSparseBooleanMatrix	Sparse representation of a boolean matrix, using HashSets
►CIMatrix< float >
►CICorrelationMatrix	Interface representing correlation and similarity matrices
►CAsymmetricCorrelationMatrix	Class for computing and storing correlations and similarities
►CBinaryDataAsymmetricCorrelationMatrix	Class with commoin routines for asymmetric correlations that are learned from binary data
CBidirectionalConditionalProbability	Class for storing and computing 'bi-directional' conditional probabilities
CConditionalProbability	Class for storing and computing conditional probabilities
►CIBinaryDataCorrelationMatrix	CorrelationMatrix that computes correlations over binary data
CBinaryDataAsymmetricCorrelationMatrix	Class with commoin routines for asymmetric correlations that are learned from binary data
►CBinaryDataSymmetricCorrelationMatrix	Class with common routines for symmetric correlations that are learned from binary data
CBinaryCosine	Class for storing cosine similarities
CCooccurrence	Class for storing and computing the co-counts
CJaccard	Class for storing and computing the Jaccard index (Tanimoto coefficient)
►CIRatingCorrelationMatrix	CorrelationMatrix that computes correlations over rating data
►CPearson	Shrunk Pearson correlation for rating data
CRatingCosine	Rating cosine similarity for rating data
►CSymmetricCorrelationMatrix	Class for computing and storing correlations and similarities
CBinaryDataSymmetricCorrelationMatrix	Class with common routines for symmetric correlations that are learned from binary data
CPearson	Shrunk Pearson correlation for rating data
►CINeedsMappings	Interface for classes that need user and item ID mappings, e.g. for recommenders that read data from external sources and thus need to know which IDs are used externally.
CExternalItemRecommender	Uses externally computed predictions
CExternalRatingPredictor	Uses externally computed predictions
►CISerializable
CDataSet	Abstract dataset class that implements some common functions
CMapping	Class to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
CPosOnlyFeedback< T >	Data structure for implicit, positive-only user feedback
CISplit< T >	generic dataset splitter interface
►CISplit< IPosOnlyFeedback >
CPosOnlyFeedbackCrossValidationSplit< T >	K-fold cross-validation split for item prediction from implicit feedback
CPosOnlyFeedbackSimpleSplit< T >	simple split for item prediction from implicit feedback
►CISplit< IRatings >
CRatingCrossValidationSplit	k-fold cross-validation split for rating prediction
CRatingsSimpleSplit	simple split for rating prediction
►CISplit< ITimedRatings >
CRatingsChronologicalSplit	chronological split for rating prediction
CRatingsPerUserChronologicalSplit	per-user chronological split for rating prediction
CItemData	Class that contains static methods for reading in implicit feedback data for ItemRecommender
CItemDataRatingThreshold	Class that contains static methods for reading in implicit feedback data for ItemRecommender, derived from rating data
CItems	Evaluation class for item recommendation
CItems	Routines for reading in the item taxonomy of the KDD Cup 2011 data
CItemsCrossValidation	Cross-validation for item recommendation
CItemsOnline	Online evaluation for rankings of items
CITransductiveItemRecommender	Interface for item recommenders that take into account some test data for training
►CIUserSimilarityProvider	Interface for classes that provide user similarities
CUserKNN	k-nearest neighbor user-based collaborative filtering
CUserKNN	Weighted user-based kNN
CKDDCupItems	Represents KDD Cup 2011 items like album, track, artist, or genre
CLogisticLoss	Utility functions for the logistic loss
CMAE	Utility functions for the mean absolute error
CMatrix< float >
CMatrixExtensions	Utilities to work with matrices
CMatrixExtensions	Utilities to work with matrices
CMemory	Memory-related tools
CModel	Class containing static routines for reading and writing recommender models
CMovieLensRatingData	Class that offers static methods for reading in MovieLens 1M and 10M rating data
CMultiCore	Utility routines for multi-core algorithms
CNDCG	Normalized discounted cumulative gain (NDCG) of a list of ranked items
CNelderMead	Nealder-Mead algorithm for finding suitable hyperparameters
COverlap	Class containing routines for computing overlaps
CPrecisionAndRecall	Precision and recall at different positions in the list
►CRandom
CRandom	Random number generator singleton class
CRatingBasedRankingCrossValidation	Cross-validation for rating-based ranking
CRatingData	Class that offers methods for reading in rating data
CRatings	Evaluation class for rating prediction
CRatings	Class that offers static methods for reading in rating data from the KDD Cup 2011 files
CRatingScale	Class containing information about the rating scale of a data set: valid rating values, minimum/maximum rating.
CRatingsCrossValidation	Cross-validation for rating prediction
CRatingsOnline	Online evaluation for rating prediction
CReciprocalRank	The reciprocal rank of a list of ranked items
CRelationData	Class that offers static methods to read (binary) relation over entities into IBooleanMatrix objects
CRMSE	Utility functions for the root mean square error (RMSE)
CSequentialDiversification	Sequential diversification
►CSparseMatrix< float >
CAsymmetricCorrelationMatrix	Class for computing and storing correlations and similarities
CSparseMatrixExtensions	Utilities to work with matrices
CStaticRatingData	Class that offers methods for reading in static rating data
►CSymmetricSparseMatrix< float >
CSymmetricCorrelationMatrix	Class for computing and storing correlations and similarities
CSkewSymmetricSparseMatrix	a skew symmetric (anti-symmetric) sparse matrix; consumes less memory
CSymmetricSparseMatrix< int >
CTimedRatingData	Class that offers methods for reading in rating data with time information
CTrack2Items	Class that offers static methods for reading in test data from the KDD Cup 2011 files
CTransductiveRatingPredictorExtensions	Helper methods for ITransductiveRatingPredictor
CUtils	Class containing utility functions
CVectorExtensions	Extensions for vector-like data
CVectorExtensions	Extensions for vector-like data
CWrap	Static methods to wrap around other code.