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
BiasedMatrixFactorizationMatrix factorization with explicit user and item bias
BiasedMatrixFactorizationMAEBiasedMatrixFactorization optimized for MAE instead of RMSE
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 optimized using BPR-Opt
CombinedList< T >Combines two List objects
CombinedRatingsCombine two IRatings objects
CorrelationMatrixClass for computing and storing correlations and similarities
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
IBooleanMatrixInterface for boolean matrices
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
IItemAttributeAwareRecommenderInterface for recommenders that take binary item attributes into account
IItemRecommenderInterface for item recommenders
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
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
ItemAttributeKNNAttribute-aware weighted item-based kNN recommender
ItemAttributeKNNK-nearest neighbor item-based collaborative filtering using cosine-similarity over the item attibutes
ItemAverageUses the average rating value of an item for prediction
ItemKNNWeighted item-based kNN
ItemKNNUnweighted k-nearest neighbor item-based collaborative filtering using cosine similarity
ItemKNNCosineWeighted item-based kNN with cosine similarity
ItemKNNPearsonWeighted item-based kNN with pearson correlation
ItemRecommendationClass that contains static methods for reading in implicit feedback data for ItemRecommender
ItemRecommenderAbstract item recommender class that loads the training data into memory
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 rating predictors that use some kind of kNN
KNNBase class for item recommenders that use some kind of kNN model
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
MatrixUtilsUtilities to work with matrices
MemoryMemory-related tools
MFAbstract class for matrix factorization based item predictors
MostPopularMost-popular item recommender
MovieLensRatingDataClass that offers static methods for reading in MovieLens 1M and 10M rating data
Pair< T, U >Generic pair class
PearsonCorrelation class for Pearson correlation
PosOnlyFeedback< T >Data structure for implicit, positive-only user feedback
PosOnlyFeedbackSimpleSplit< T >Simple split for item prediction from implicit feedback
RandomRandom item recommender for use as experimental baseline
RandomDraws random values from a normal distibuted using a simple rejection method
RatingCorrelationMatrixCorrelationMatrix that computes correlations over rating data
RatingCrossValidationSplitK-fold split for rating prediction
RatingPredictionClass that contains static methods for rating prediction
RatingPredictionClass that offers methods for reading in rating data
RatingPredictionStaticClass that offers methods for reading in static rating data
RatingPredictorAbstract class for rating predictors that keep the rating data in memory for training (and possibly prediction)
RatingsData structure for storing ratings
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
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
StaticRatingsArray-based storage for rating data
StaticRatingsWithDateTimeRating data structure for ratings with time stamps
SymmetricSparseMatrix< T >Symmetric sparse matrix; consumes less memory
Triple< T, U, V >Generic triple class
UserAttributeKNNK-nearest neighbor user-based collaborative filtering using cosine-similarity over the user attibutes
UserAttributeKNNWeighted kNN recommender based on user attributes
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
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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