AttributeData | Class that offers static methods to read (binary) attribute data into SparseBooleanMatrix objects |
BiasedMatrixFactorization | Matrix factorization with explicit user and item bias, learning is performed by stochastic gradient descent |
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 (ranking) 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 |
FactorWiseMatrixFactorization | Matrix factorization with factor-wise learning |
GlobalAverage | Uses the average rating value over all ratings for prediction |
IBooleanMatrix | Interface for boolean matrices |
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 recommenders that take binary item attributes into account |
IItemRecommender | Interface for item recommenders |
IItemRelationAwareRecommender | Interface for recommenders 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 |
IPosOnlyFeedback | Interface for implicit, positive-only user feedback |
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 | Unweighted k-nearest neighbor item-based collaborative filtering using cosine similarity |
ItemKNN | Weighted item-based kNN |
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 |
ItemRecommendationRatingThreshold | Class that contains static methods for reading in implicit feedback data for ItemRecommender |
ItemRecommender | Abstract item recommender class that loads the (positive-only implicit feedback) training data into memory |
ITimedRatings | Interface for rating datasets with time information |
IUserAttributeAwareRecommender | Interface for recommenderss that take binary user attributes into account |
IUserRelationAwareRecommender | Interface for recommenders that take a binary relation over users into account |
Jaccard | Class for storing the Jaccard index |
KNN | Base class for item recommenders that use some kind of kNN model |
KNN | Base 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 |
MatrixFactorization | Simple matrix factorization class, learning is performed by stochastic gradient descent |
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< 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 |
Prediction | Class that contains static methods for rating prediction |
Random | Random item recommender for use as experimental baseline |
Random | Draws random values from a normal distibuted using a simple rejection method |
RatingCorrelationMatrix | CorrelationMatrix that computes correlations over rating data |
RatingCrossValidationSplit | K-fold split 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, using HashSets |
SparseBooleanMatrixBinarySearch | Sparse representation of a boolean matrix, using binary search (memory efficient) |
SparseBooleanMatrixStatic | Sparse 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 |
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 |
TimedRatings | Class that offers methods for reading in rating data |
TimedRatings | Data structure for storing ratings |
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 based on user attributes |
UserAverage | Uses the average rating value of a user for predictions |
UserItemBaseline | Baseline method for rating prediction |
UserKNN | Weighted user-based kNN |
UserKNN | K-nearest neighbor user-based collaborative filtering using cosine-similarity (unweighted) |
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 |