Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
AttributeDataClass that offers static methods to read (binary) attribute data into SparseBooleanMatrix objects
AUCArea under the ROC curve (AUC) of a list of ranked items
BiasedMatrixFactorizationMatrix factorization with explicit user and item bias, learning is performed by stochastic gradient descent
BinaryCosineClass for storing cosine similarities
BinaryDataCorrelationMatrixCorrelationMatrix that computes correlations over binary data
BiPolarSlopeOneBi-polar frequency-weighted Slope-One rating prediction
BPR_LinearLinear model optimized for BPR
BPRMFMatrix factorization model for item prediction (ranking) optimized for BPR
CombinedList< T >Combines two List objects
CombinedRatingsCombine two IRatings objects
ConstantsStatic class containing constants used by the MyMediaLite Input/Output routines
CorrelationMatrixClass for computing and storing correlations and similarities
DataSetAbstract dataset class that implements some common functions
EnsembleAbtract class for combining several prediction methods
EntityAverageAbstract class that uses an average (by entity) rating value for predictions
EntityMappingClass to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
FactorWiseMatrixFactorizationMatrix factorization with factor-wise learning
GlobalAverageUses the average rating value over all ratings for prediction
HandlersClass containing handler functions, e.g. exception handlers
IBooleanMatrixInterface for boolean matrices
IDataSetInterface for different kinds of collaborative filtering data sets
IdentityMappingIdentity mapping for entity IDs: Every original ID is mapped to itself
IEntityMappingInterface to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
IIncrementalItemRecommenderInterface for item recommenders
IIncrementalRatingPredictorInterface for rating predictors which support incremental training
IItemAttributeAwareRecommenderInterface for recommenders that take binary item attributes into account
IItemRelationAwareRecommenderInterface for recommenders that take a binary relation over items into account
IIterativeModelInterface representing iteratively trained models
IMatrix< T >Generic interface for matrix data types
IMatrixUtilsUtilities to work with matrices
IncrementalItemRecommenderBase class for item recommenders that support incremental updates
IncrementalRatingPredictorBase class for rating predictors that support incremental training
IPosOnlyFeedbackInterface for implicit, positive-only user feedback
IRatingPredictorInterface for rating predictors
IRatingsInterface for rating datasets
IRecommenderGeneric interface for simple recommenders
ISplit< T >Generic dataset splitter interface
ItemAttributeKNNK-nearest neighbor item-based collaborative filtering using cosine-similarity over the item attibutes
ItemAttributeKNNAttribute-aware weighted item-based kNN recommender
ItemAverageUses the average rating value of an item for prediction
ItemDataClass that contains static methods for reading in implicit feedback data for ItemRecommender
ItemDataRatingThresholdClass that contains static methods for reading in implicit feedback data for ItemRecommender
ItemKNNUnweighted k-nearest neighbor item-based collaborative filtering using cosine similarity
ItemKNNWeighted item-based kNN
ItemKNNCosineWeighted item-based kNN with cosine similarity
ItemKNNPearsonWeighted item-based kNN with pearson correlation
ItemRecommenderAbstract item recommender class that loads the (positive-only implicit feedback) training data into memory and provides flexible access to it
ItemsEvaluation class for item recommendation
ItemsCrossValidationCross-validation for item recommendation
ItemsOnlineOnline evaluation for rankings of items
ITimedRatingsInterface for rating datasets with time information
IUserAttributeAwareRecommenderInterface for recommenderss that take binary user attributes into account
IUserRelationAwareRecommenderInterface for recommenders that take a binary relation over users into account
JaccardClass for storing the Jaccard index
KNNBase class for item recommenders that use some kind of k-nearest neighbors (kNN) model
KNNBase class for rating predictors that use some kind of kNN
ListProxy< T >Proxy class that allows access to selected elements of an underlying list data structure
Matrix< T >Class for storing dense matrices
MatrixFactorizationSimple matrix factorization class, learning is performed by stochastic gradient descent
MatrixUtilsUtilities to work with matrices
MemoryMemory-related tools
MFAbstract class for matrix factorization based item predictors
ModelClass containing static routines for reading and writing recommender models
MostPopularMost-popular item recommender
MovieLensRatingDataClass that offers static methods for reading in MovieLens 1M and 10M rating data
NDCGNormalized discounted cumulative gain (NDCG) of a list of ranked items
NumberFileRoutines to read lists of numbers from text files
Pair< T, U >Generic pair class
PearsonCorrelation class for Pearson correlation
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
PrecisionAndRecallPrecision and recall at different positions in the list
PredictionClass that contains static methods for item prediction
PredictionClass that contains static methods for rating prediction
RandomRandom item recommender for use as experimental baseline
RandomRandom number generator singleton class
RatingCorrelationMatrixCorrelationMatrix that computes correlations over rating data
RatingCrossValidationSplitK-fold split for rating prediction
RatingDataClass that offers methods for reading in rating data
RatingPredictorAbstract class for rating predictors that keep the rating data in memory for training (and possibly prediction)
RatingsData structure for storing ratings
RatingsEvaluation class for rating prediction
RatingsCrossValidationCross-validation for rating prediction
RatingsOnlineOnline evaluation for rating prediction
RatingsProxyData structure that allows access to selected entries of a rating data structure
RatingsSimpleSplitSimple split for rating prediction
RatingsWithDateTimeRating data structure for ratings with time stamps
ReciprocalRankThe reciprocal rank of a list of ranked items
RecommenderHelper class with utility methods for handling recommenders
RecommenderParametersClass for key-value pair string processing
RelationDataClass that offers static methods to read (binary) relation over entities into SparseBooleanMatrix objects
SkewSymmetricSparseMatrixSkew symmetric (anti-symmetric) sparse matrix; consumes less memory
SlopeOneFrequency-weighted Slope-One rating prediction
SparseBooleanMatrixSparse representation of a boolean matrix, using HashSets
SparseBooleanMatrixBinarySearchSparse representation of a boolean matrix, using binary search (memory efficient)
SparseBooleanMatrixStaticSparse representation of a boolean matrix, using binary search (memory efficient)
SparseMatrix< T >Class for storing sparse matrices
SparseVector< T >Class for storing sparse vectors. Indexes are zero-based
StaticByteRatingsArray-based storage for rating data
StaticFloatRatingsArray-based storage for rating data
StaticRatingDataClass that offers methods for reading in static rating data
StaticRatingsArray-based storage for rating data
StaticRatingsWithDateTimeRating data structure for ratings with time stamps
SymmetricSparseMatrix< T >Symmetric sparse matrix; consumes less memory
TimedRatingsClass that offers methods for reading in rating data
TimedRatingsData structure for storing ratings
Triple< T, U, V >Generic triple class
UserAttributeKNNWeighted kNN recommender based on user attributes
UserAttributeKNNK-nearest neighbor user-based collaborative filtering using cosine-similarity over the user attibutes
UserAverageUses the average rating value of a user for predictions
UserItemBaselineBaseline method for rating prediction
UserKNNK-nearest neighbor user-based collaborative filtering using cosine-similarity (unweighted)
UserKNNWeighted user-based kNN
UserKNNCosineWeighted user-based kNN with cosine similarity
UserKNNPearsonWeighted user-based kNN with Pearson correlation
UtilsClass containing utility functions
VectorUtilsTools for vector-like data
WeightedEnsembleCombining several predictors with a weighted ensemble
WeightedItemWeighted items class
WeightedItemKNNWeighted k-nearest neighbor item-based collaborative filtering using cosine similarity
WeightedUserKNNWeighted k-nearest neighbor user-based collaborative filtering using cosine-similarity
WRMFWeighted matrix factorization method proposed by Hu et al. and Pan et al
ZeroConstant item recommender for use as experimental baseline. Always predicts a score of zero
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