Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

AsymmetricCorrelationMatrix	Class for computing and storing correlations and similarities
AttributeData	Class that offers static methods to read (binary) attribute data into IBooleanMatrix objects
AUC	Area under the ROC curve (AUC) of a list of ranked items
Average	Group recommender that averages user scores
BiasedMatrixFactorization	Matrix factorization with explicit user and item bias, learning is performed by stochastic gradient descent
BidirectionalConditionalProbability	Class for storing and computing 'bi-directional' conditional probabilities
BinaryCosine	Class for storing cosine similarities
BinaryDataAsymmetricCorrelationMatrix	Class with commoin routines for asymmetric correlations that are learned from binary data
BinaryDataSymmetricCorrelationMatrix	Class with common routines for symmetric correlations that are learned from binary data
BiPolarSlopeOne	Bi-polar frequency-weighted Slope-One rating prediction
BPRLinear	Linear model optimized for BPR
BPRMF	Matrix factorization model for item prediction (ranking) optimized for BPR
BPRMF_ItemMapping	BPR-MF with item mapping learned by regularized least-squares regression
BPRMF_ItemMapping_Optimal	Item attribute to latent factor mapping, optimized for BPR loss
BPRMF_ItemMappingKNN	BPR-MF with item mapping learned by kNN
BPRMF_ItemMappingSVR	BPR-MF with item mapping learned by support-vector regression (SVR)
BPRMF_Mapping	Base class for BPR-MF plus attribute-to-factor mapping
BPRMF_Mapping	BPR-MF with attribute-to-factor mapping
BPRMF_UserMapping	User attribute to latent factor mapping for BPR-MF, optimized for RMSE on the latent factors
BPRMF_UserMapping_Optimal	User attribute to latent factor mapping for BPR-MF, optimized for BPR loss
CLiMF	Collaborative Less-is-More Filtering Matrix Factorization
CoClustering	Co-clustering for rating prediction
CombinedList< T >	Combines two List objects
CombinedRatings	Combine two IRatings objects
ConditionalProbability	Class for storing and computing conditional probabilities
Constant	Uses a constant rating value for prediction
Constants	Static class containing constants used by the MyMediaLite Input/Output routines
Cooccurrence	Class for storing and computing the co-counts
DataReaderExtensions	Extension methods for IDataReader objects
DataSet	Abstract dataset class that implements some common functions
Ensemble	Abtract class for combining several prediction methods
EntityAverage	Abstract class that uses an average (by entity) rating value for predictions
EntityMappingExtensions	I/O routines for classes implementing the IEntityMapping interface
Extensions	Class that contains static methods for rating prediction
Extensions	Helper class with utility methods for handling recommenders
Extensions	Class containing utility functions for group recommenders
Extensions	Extension methods for dataset statistics
Extensions	Extension methods for correlation matrices
Extensions	Class that contains static methods for item prediction
FactorWiseMatrixFactorization	Matrix factorization with factor-wise learning
FileSerializer	Static class for serializing objects to binary files
FoldIn	Fold-in evaluation
FoldInRatingPredictorExtensions	Extension methods for IFoldInRatingPredictor
GlobalAverage	Uses the average rating value over all ratings for prediction
GridSearch	Grid search for finding suitable hyperparameters
GroupRecommender	Base class for group recommenders
Groups	Evaluation class for group recommendation
Handlers	Class containing handler functions, e.g. exception handlers
IBinaryDataCorrelationMatrix	CorrelationMatrix that computes correlations over binary data
IBooleanMatrix	Interface for boolean matrices
ICorrelationMatrix	Interface representing correlation and similarity matrices
IDataSet	Interface for different kinds of collaborative filtering data sets
IdentityMapping	Identity mapping for entity IDs: Every original ID is mapped to itself
IFoldInItemRecommender	Item recommender that allows folding in new users
IFoldInRatingPredictor	Rating predictor that allows folding in new users
IGroupRecommender	Interface for group recommenders
IHyperParameterSearch	Interface for classes that perform hyper-parameter search
IIncrementalItemRecommender	Interface for item recommenders
IIncrementalRatingPredictor	Interface for rating predictors which support incremental training
IIncrementalRecommender	Interface for recommenders that support incremental model updates
IItemAttributeAwareRecommender	Interface for recommenders that take binary item attributes into account
IItemRelationAwareRecommender	Interface for recommenders that take a binary relation over items into account
IItemSimilarityProvider	Interface for classes that provide item similarities
IIterativeModel	Interface representing iteratively trained models
IMapping	Interface to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
IMatrix< T >	Generic interface for matrix data types
IncrementalItemRecommender	Base class for item recommenders that support incremental updates
IncrementalRatingPredictor	Base class for rating predictors that support incremental training
IPosOnlyFeedback	Interface for implicit, positive-only user feedback
IRatingCorrelationMatrix	CorrelationMatrix that computes correlations over rating data
IRatingPredictor	Interface for rating predictors
IRatings	Interface for rating datasets
IRecommender	Generic interface for simple recommenders
ISplit< T >	Generic dataset splitter interface
ItemAttributeKNN	K-nearest neighbor (kNN) item-based collaborative filtering using the correlation of the item attibutes
ItemAttributeKNN	Attribute-aware weighted item-based kNN recommender
ItemAttributeSVM	Content-based filtering using one support-vector machine (SVM) per user
ItemAverage	Uses the average rating value of an item for prediction
ItemData	Class that contains static methods for reading in implicit feedback data for ItemRecommender
ItemDataRatingThreshold	Class that contains static methods for reading in implicit feedback data for ItemRecommender, derived from rating data
ItemKNN	K-nearest neighbor (kNN) item-based collaborative filtering
ItemKNN	Weighted item-based kNN
ItemRecommendationEvaluationResults	Item recommendation evaluation results
ItemRecommender	Abstract item recommender class that loads the (positive-only implicit feedback) training data into memory and provides flexible access to it
Items	Evaluation class for item recommendation
Items	Routines for reading in the item taxonomy of the KDD Cup 2011 data
ItemsCrossValidation	Cross-validation for item recommendation
ItemsOnline	Online evaluation for rankings of items
ITimeAwareRatingPredictor	Interface for time-aware rating predictors
ITimedDataSet	Interface for data sets with time information
ITimedRatings	Interface for rating datasets with time information
ITransductiveItemRecommender	Interface for item recommenders that take into account some test data for training
ITransductiveRatingPredictor	Rating predictor that knows beforehand what it will have to rate
IUserAttributeAwareRecommender	Interface for recommenderss that take binary user attributes into account
IUserRelationAwareRecommender	Interface for recommenders that take a binary relation over users into account
IUserSimilarityProvider	Interface for classes that provide user similarities
Jaccard	Class for storing and computing the Jaccard index (Tanimoto coefficient)
KDDCupItems	Represents KDD Cup 2011 items like album, track, artist, or genre
KNN	Base class for rating predictors that use some kind of kNN
KNN	Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model
LatentFeatureLogLinearModel	Latent-feature log linear model
ListProxy< T >	Proxy class that allows access to selected elements of an underlying list data structure
LogisticLoss	Utility functions for the logistic loss
MAE	Utility functions for the mean absolute error
Mapping	Class to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
Matrix< T >	Class for storing dense matrices
MatrixExtensions	Utilities to work with matrices
MatrixExtensions	Utilities to work with matrices
MatrixFactorization	Simple matrix factorization class, learning is performed by stochastic gradient descent (SGD)
Maximum	Group recommender that takes the maximum user score as the group score
Memory	Memory-related tools
MF	Abstract class for matrix factorization based item predictors
Minimum	Group recommender that takes the minimum user score as the group score
Model	Class containing static routines for reading and writing recommender models
MostPopular	Most-popular item recommender
MostPopularByAttributes	Recommend most popular items by attribute
MovieLensRatingData	Class that offers static methods for reading in MovieLens 1M and 10M rating data
MultiCore	Class containing utility routines for multi-core algorithms
MultiCoreBPRMF	Matrix factorization for BPR on multiple cores
NaiveBayes	Attribute-aware rating predictor using Naive Bayes
NDCG	Normalized discounted cumulative gain (NDCG) of a list of ranked items
NelderMead	Nealder-Mead algorithm for finding suitable hyperparameters
Overlap	Class containing routines for computing overlaps
PairwiseWins	A simple Condorcet-style voting mechanism
Pearson	Shrunk Pearson correlation for rating data
PosOnlyFeedback< T >	Data structure for implicit, positive-only user feedback
PosOnlyFeedbackCrossValidationSplit< T >	K-fold cross-validation split for item prediction from implicit feedback
PosOnlyFeedbackSimpleSplit< T >	Simple split for item prediction from implicit feedback
PrecisionAndRecall	Precision and recall at different positions in the list
Random	Random item recommender for use as experimental baseline
Random	Random number generator singleton class
Random	Uses a random rating value for prediction
RatingCrossValidationSplit	K-fold cross-validation split for rating prediction
RatingData	Class that offers methods for reading in rating data
RatingPredictionEvaluationResults	Rating prediction evaluation results
RatingPredictor	Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction)
Ratings	Evaluation class for rating prediction
Ratings	Data structure for storing ratings
Ratings	Class that offers static methods for reading in rating data from the KDD Cup 2011 files
RatingScale	Class containing information about the rating scale of a data set: valid rating values, minimum/maximum rating
RatingsChronologicalSplit	Chronological split for rating prediction
RatingsCrossValidation	Cross-validation for rating prediction
RatingsOnline	Online evaluation for rating prediction
RatingsPerUserChronologicalSplit	Per-user chronological split for rating prediction
RatingsProxy	Data structure that allows access to selected entries of a rating data structure
RatingsSimpleSplit	Simple split for rating prediction
ReciprocalRank	The reciprocal rank of a list of ranked items
Recommender	Abstract recommender class implementing default behaviors
RecommenderParameters	Class for key-value pair string processing
RelationData	Class that offers static methods to read (binary) relation over entities into IBooleanMatrix objects
RMSE	Utility functions for the root mean square error (RMSE)
SequentialDiversification	Sequential diversification
SigmoidCombinedAsymmetricFactorModel	Asymmetric factor model which represents items in terms of the users that rated them, and users in terms of the items they rated
SigmoidItemAsymmetricFactorModel	Asymmetric factor model
SigmoidSVDPlusPlus	SVD++: Matrix factorization that also takes into account _what_ users have rated; variant that uses a sigmoid function
SigmoidUserAsymmetricFactorModel	Asymmetric factor model which represents items in terms of the users that rated them
SkewSymmetricSparseMatrix	Skew symmetric (anti-symmetric) sparse matrix; consumes less memory
SlopeOne	Frequency-weighted Slope-One rating prediction
SocialMF	Social-network-aware matrix factorization
SoftMarginRankingMF	Matrix factorization model for item prediction optimized for a soft margin (hinge) ranking loss, using stochastic gradient descent (as in BPR-MF)
SparseBooleanMatrix	Sparse representation of a boolean matrix, using HashSets
SparseMatrix< T >	Class for storing sparse matrices
SparseMatrixExtensions	Utilities to work with matrices
StaticByteRatings	Array-based storage for rating data
StaticRatingData	Class that offers methods for reading in static rating data
StaticRatings	Array-based storage for rating data
SVDPlusPlus	SVD++: Matrix factorization that also takes into account _what_ users have rated
SymmetricCorrelationMatrix	Class for computing and storing correlations and similarities
SymmetricMatrix< T >	Class for storing dense matrices
SymmetricSparseMatrix< T >	Symmetric sparse matrix; consumes less memory
TimeAwareBaseline	Time-aware bias model
TimeAwareBaselineWithFrequencies	Time-aware bias model with frequencies
TimeAwareRatingPredictor	Abstract class for time-aware rating predictors
TimedRatingData	Class that offers methods for reading in rating data with time information
TimedRatings	Data structure for storing ratings with time information
TimedRatingsProxy	Data structure that allows access to selected entries of a timed rating data structure
Track2Items	Class that offers static methods for reading in test data from the KDD Cup 2011 files
TransductiveRatingPredictorExtensions	Helper methods for ITransductiveRatingPredictor
UserAttributeKNN	Weighted kNN recommender based on user attributes
UserAttributeKNN	K-nearest neighbor (kNN) user-based collaborative filtering using the correlation of the user attibutes
UserAverage	Uses the average rating value of a user for predictions
UserItemBaseline	Baseline method for rating prediction
UserKNN	K-nearest neighbor user-based collaborative filtering
UserKNN	Weighted user-based kNN
Utils	Class containing utility functions
VectorExtensions	Extensions for vector-like data
VectorExtensions	Extensions for vector-like data
WeightedAverage	Group recommender that averages user scores weighted by the rating frequency of the individual users
WeightedBPRMF	Weigthed BPR-MF with frequency-adjusted sampling
WeightedEnsemble	Combining several predictors with a weighted ensemble
Wrap	Static methods to wrap around other code
WRMF	Weighted matrix factorization method proposed by Hu et al. and Pan et al
Zero	Constant item recommender for use as experimental baseline. Always predicts a score of zero