| AttributeData | Class that offers static methods to read (binary) attribute data into SparseBooleanMatrix objects |
| AUC | Area under the ROC curve (AUC) of a list of ranked items |
| 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 for BPR |
| CombinedList< T > | Combines two List objects |
| CombinedRatings | Combine two IRatings objects |
| Constants | Static class containing constants used by the MyMediaLite Input/Output routines |
| CorrelationMatrix | Class for computing and storing correlations and similarities |
| 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 |
| 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 |
| Handlers | Class containing handler functions, e.g. exception handlers |
| IBooleanMatrix | Interface for boolean matrices |
| IDataSet | Interface for different kinds of collaborative filtering data sets |
| 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 |
| IIncrementalItemRecommender | Interface for item recommenders |
| IIncrementalRatingPredictor | Interface for rating predictors which support incremental training |
| IItemAttributeAwareRecommender | Interface for recommenders that take binary item attributes into account |
| 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 |
| 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 |
| 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 item-based collaborative filtering using cosine-similarity over the item attibutes |
| ItemAttributeKNN | Attribute-aware weighted item-based kNN recommender |
| 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 |
| 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 |
| 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 |
| ItemsCrossValidation | Cross-validation for item recommendation |
| ItemsOnline | Online evaluation for rankings of items |
| 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 k-nearest neighbors (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 |
| Model | Class containing static routines for reading and writing recommender models |
| MostPopular | Most-popular item recommender |
| MovieLensRatingData | Class that offers static methods for reading in MovieLens 1M and 10M rating data |
| NDCG | Normalized discounted cumulative gain (NDCG) of a list of ranked items |
| NumberFile | Routines to read lists of numbers from text files |
| 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 |
| PrecisionAndRecall | Precision and recall at different positions in the list |
| Prediction | Class that contains static methods for item prediction |
| Prediction | Class that contains static methods for rating prediction |
| Random | Random item recommender for use as experimental baseline |
| Random | Random number generator singleton class |
| RatingCorrelationMatrix | CorrelationMatrix that computes correlations over rating data |
| RatingCrossValidationSplit | K-fold split for rating prediction |
| RatingData | Class that offers methods for reading in 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 |
| Ratings | Evaluation class for rating prediction |
| RatingsCrossValidation | Cross-validation for rating prediction |
| RatingsOnline | Online evaluation for rating prediction |
| 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 |
| ReciprocalRank | The reciprocal rank of a list of ranked items |
| 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 |
| StaticRatingData | Class that offers methods for reading in static 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 | Weighted kNN recommender based on user attributes |
| UserAttributeKNN | K-nearest neighbor user-based collaborative filtering using cosine-similarity over 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 using cosine-similarity (unweighted) |
| UserKNN | Weighted user-based kNN |
| UserKNNCosine | Weighted user-based kNN with cosine similarity |
| UserKNNPearson | Weighted user-based kNN with Pearson correlation |
| Utils | Class containing utility functions |
| 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 |
