Class List

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

AttributeData	Class that offers static methods to read (binary) attribute data into SparseBooleanMatrix objects
BiasedMatrixFactorization	Matrix factorization engine with explicit user and item bias
BinaryCosine	Class for storing cosine similarities
BinaryDataCorrelationMatrix	CorrelationMatrix that computes correlations over binary data
BiPolarSlopeOne	Bi-polar frequency-weighted Slope-One rating prediction
BPR_Linear	Linear model optimized for BPR
BPRMF	Matrix factorization model for item prediction optimized using BPR-Opt
CombinedList< T >	Combines two List objects
CombinedRatings	Combine two IRatings objects
CorrelationMatrix	Class for computing and storing correlations and similarities
Ensemble	Abtract class for combining several prediction methods
EntityAverage	Abstract class that uses an average (by entity) rating value for predictions
EntityMapping	Class to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
GlobalAverage	Uses the average rating value over all ratings for prediction
IdentityMapping	Identity mapping for entity IDs: Every original ID is mapped to itself
IEntityMapping	Interface to map external entity IDs to internal ones to ensure that there are no gaps in the numbering
IItemAttributeAwareRecommender	Interface for recommender engines that take binary item attributes into account
IItemRecommender	Interface for item recommenders
IItemRelationAwareRecommender	Interface for recommender engines that take a binary relation over items into account
IIterativeModel	Interface representing iteratively trained models
IMatrix< T >	Generic interface for matrix data types
IMatrixUtils	Utilities to work with matrices
IRatingPredictor	Interface for rating predictors
IRatings	Interface for rating datasets
IRecommender	Generic interface for simple recommenders
ISplit< T >	Generic dataset splitter interface
ItemAttributeKNN	Attribute-aware weighted item-based kNN recommender
ItemAttributeKNN	K-nearest neighbor item-based collaborative filtering using cosine-similarity over the item attibutes
ItemAverage	Uses the average rating value of an item for prediction
ItemKNN	Weighted item-based kNN engine
ItemKNN	Unweighted k-nearest neighbor item-based collaborative filtering using cosine similarity
ItemKNNCosine	Weighted item-based kNN with cosine similarity
ItemKNNPearson	Weighted item-based kNN with pearson correlation
ItemRecommendation	Class that contains static methods for reading in implicit feedback data for ItemRecommender engines
ItemRecommender	Abstract item recommender class that loads the training data into memory
IUserAttributeAwareRecommender	Interface for recommender engines that take binary user attributes into account
IUserRelationAwareRecommender	Interface for recommender engines that take a binary relation over users into account
Jaccard	Class for storing the Jaccard index
KNN	Base class for rating predictors that use some kind of kNN
KNN	Base 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
MatrixFactorization	Simple matrix factorization class
MatrixUtils	Utilities to work with matrices
Memory	Memory-related tools
MF	Abstract class for matrix factorization based item predictors
MostPopular	Most-popular item recommender
MovieLensRatingData	Class that offers static methods for reading in MovieLens 1M and 10M rating data
Pair< T, U >	Generic pair class
Pearson	Correlation class for Pearson correlation
PosOnlyFeedback	Data structure for implicit, positive-only user feedback
PosOnlyFeedbackSimpleSplit	Simple split for item prediction from implicit feedback
Random	Draws random values from a normal distibuted using a simple rejection method
Random	Random item recommender for use as experimental baseline
RatingCorrelationMatrix	CorrelationMatrix that computes correlations over rating data
RatingCrossValidationSplit	K-fold split for rating prediction
RatingPrediction	Class that contains static methods for rating prediction
RatingPrediction	Class that offers methods for reading in rating data
RatingPredictionStatic	Class that offers methods for reading in static rating data
RatingPredictor	Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction)
Ratings	Data structure for storing ratings
RatingsProxy	Data structure that allows access to selected entries of a rating data structure
RatingsSimpleSplit	Simple split for rating prediction
RatingsWithDateTime	Rating data structure for ratings with time stamps
Recommender	Helper class with utility methods for handling recommenders
RecommenderParameters	Class for key-value pair string processing
RelationData	Class that offers static methods to read (binary) relation over entities into SparseBooleanMatrix objects
SkewSymmetricSparseMatrix	Skew symmetric (anti-symmetric) sparse matrix; consumes less memory
SlopeOne	Frequency-weighted Slope-One rating prediction
SparseBooleanMatrix	Sparse representation of a boolean matrix
SparseMatrix< T >	Class for storing sparse matrices
SparseVector< T >	Class for storing sparse vectors. Indexes are zero-based
StaticByteRatings	Array-based storage for rating data
StaticFloatRatings	Array-based storage for rating data
StaticRatings	Array-based storage for rating data
StaticRatingsWithDateTime	Rating data structure for ratings with time stamps
SymmetricSparseMatrix< T >	Symmetric sparse matrix; consumes less memory
Triple< T, U, V >	Generic triple class
UserAttributeKNN	K-nearest neighbor user-based collaborative filtering using cosine-similarity over the user attibutes
UserAttributeKNN	Weighted kNN recommender engine based on user attributes
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 using cosine-similarity (unweighted)
UserKNN	Weighted user-based kNN engine
UserKNNCosine	Weighted user-based kNN with cosine similarity
UserKNNPearson	Weighted user-based kNN with Pearson correlation
VectorUtils	Tools for vector-like data
WeightedEnsemble	Combining several predictors with a weighted ensemble
WeightedItem	Weighted items class
WeightedItemKNN	Weighted k-nearest neighbor item-based collaborative filtering using cosine similarity
WeightedUserKNN	Weighted k-nearest neighbor user-based collaborative filtering using cosine-similarity
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