AsymmetricCorrelationMatrix | Class for computing and storing correlations and similarities |
AttributeData | Class that offers static methods to read (binary) attribute data into IBooleanMatrix objects |
AUC | Area under the ROC curve (AUC) of a list of ranked items |
Average | Group recommender that averages user scores |
BiasedMatrixFactorization | Matrix factorization with explicit user and item bias, learning is performed by stochastic gradient descent |
BidirectionalConditionalProbability | Class for storing and computing 'bi-directional' conditional probabilities |
BinaryCosine | Class for storing cosine similarities |
BinaryDataAsymmetricCorrelationMatrix | Class with commoin routines for asymmetric correlations that are learned from binary data |
BinaryDataSymmetricCorrelationMatrix | Class with common routines for symmetric correlations that are learned from binary data |
BiPolarSlopeOne | Bi-polar frequency-weighted Slope-One rating prediction |
BPRLinear | Linear model optimized for BPR |
BPRMF | Matrix factorization model for item prediction (ranking) optimized for BPR |
BPRMF_ItemMapping | BPR-MF with item mapping learned by regularized least-squares regression |
BPRMF_ItemMapping_Optimal | Item attribute to latent factor mapping, optimized for BPR loss |
BPRMF_ItemMappingKNN | BPR-MF with item mapping learned by kNN |
BPRMF_ItemMappingSVR | BPR-MF with item mapping learned by support-vector regression (SVR) |
BPRMF_Mapping | Base class for BPR-MF plus attribute-to-factor mapping |
BPRMF_Mapping | BPR-MF with attribute-to-factor mapping |
BPRMF_UserMapping | User attribute to latent factor mapping for BPR-MF, optimized for RMSE on the latent factors |
BPRMF_UserMapping_Optimal | User attribute to latent factor mapping for BPR-MF, optimized for BPR loss |
CoClustering | Co-clustering for rating prediction |
CombinedList< T > | Combines two List objects |
CombinedRatings | Combine two IRatings objects |
ConditionalProbability | Class for storing and computing conditional probabilities |
Constant | Uses a constant rating value for prediction |
Constants | Static class containing constants used by the MyMediaLite Input/Output routines |
Cooccurrence | Class for storing and computing the co-counts |
DataReaderExtensions | Extension methods for IDataReader objects |
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 |
EntityMappingExtensions | I/O routines for classes implementing the IEntityMapping interface |
Extensions | Class that contains static methods for rating prediction |
Extensions | Helper class with utility methods for handling recommenders |
Extensions | Class containing utility functions for group recommenders |
Extensions | Extension methods for dataset statistics |
Extensions | Extension methods for correlation matrices |
Extensions | Class that contains static methods for item prediction |
FactorWiseMatrixFactorization | Matrix factorization with factor-wise learning |
FileSerializer | Static class for serializing objects to binary files |
FoldIn | Fold-in evaluation |
FoldInRatingPredictorExtensions | Extension methods for IFoldInRatingPredictor |
GlobalAverage | Uses the average rating value over all ratings for prediction |
GridSearch | Grid search for finding suitable hyperparameters |
GroupRecommender | Base class for group recommenders |
Groups | Evaluation class for group recommendation |
Handlers | Class containing handler functions, e.g. exception handlers |
IBinaryDataCorrelationMatrix | CorrelationMatrix that computes correlations over binary data |
IBooleanMatrix | Interface for boolean matrices |
ICorrelationMatrix | Interface representing correlation and similarity matrices |
IDataSet | Interface for different kinds of collaborative filtering data sets |
IdentityMapping | Identity mapping for entity IDs: Every original ID is mapped to itself |
IFoldInItemRecommender | Item recommender that allows folding in new users |
IFoldInRatingPredictor | Rating predictor that allows folding in new users |
IGroupRecommender | Interface for group recommenders |
IHyperParameterSearch | Interface for classes that perform hyper-parameter search |
IIncrementalItemRecommender | Interface for item recommenders |
IIncrementalRatingPredictor | Interface for rating predictors which support incremental training |
IIncrementalRecommender | Interface for recommenders that support incremental model updates |
IItemAttributeAwareRecommender | Interface for recommenders that take binary item attributes into account |
IItemRelationAwareRecommender | Interface for recommenders that take a binary relation over items into account |
IItemSimilarityProvider | Interface for classes that provide item similarities |
IIterativeModel | Interface representing iteratively trained models |
IMapping | Interface to map external entity IDs to internal ones to ensure that there are no gaps in the numbering |
IMatrix< T > | Generic interface for matrix data types |
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 |
IRatingCorrelationMatrix | CorrelationMatrix that computes correlations over rating data |
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 (kNN) item-based collaborative filtering using the correlation of the item attibutes |
ItemAttributeKNN | Attribute-aware weighted item-based kNN recommender |
ItemAttributeSVM | Content-based filtering using one support-vector machine (SVM) per user |
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, derived from rating data |
ItemKNN | K-nearest neighbor (kNN) item-based collaborative filtering |
ItemKNN | Weighted item-based kNN |
ItemRecommendationEvaluationResults | Item recommendation evaluation results |
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 |
Items | Routines for reading in the item taxonomy of the KDD Cup 2011 data |
ItemsCrossValidation | Cross-validation for item recommendation |
ItemsOnline | Online evaluation for rankings of items |
ITimeAwareRatingPredictor | Interface for time-aware rating predictors |
ITimedDataSet | Interface for data sets with time information |
ITimedRatings | Interface for rating datasets with time information |
ITransductiveItemRecommender | Interface for item recommenders that take into account some test data for training |
ITransductiveRatingPredictor | Rating predictor that knows beforehand what it will have to rate |
IUserAttributeAwareRecommender | Interface for recommenderss that take binary user attributes into account |
IUserRelationAwareRecommender | Interface for recommenders that take a binary relation over users into account |
IUserSimilarityProvider | Interface for classes that provide user similarities |
Jaccard | Class for storing and computing the Jaccard index (Tanimoto coefficient) |
KDDCupItems | Represents KDD Cup 2011 items like album, track, artist, or genre |
KNN | Base class for rating predictors that use some kind of kNN |
KNN | Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model |
LatentFeatureLogLinearModel | Latent-feature log linear model |
ListProxy< T > | Proxy class that allows access to selected elements of an underlying list data structure |
LogisticLoss | Utility functions for the logistic loss |
MAE | Utility functions for the mean absolute error |
Mapping | Class to map external entity IDs to internal ones to ensure that there are no gaps in the numbering |
Matrix< T > | Class for storing dense matrices |
MatrixExtensions | Utilities to work with matrices |
MatrixExtensions | Utilities to work with matrices |
MatrixFactorization | Simple matrix factorization class, learning is performed by stochastic gradient descent |
Maximum | Group recommender that takes the maximum user score as the group score |
Memory | Memory-related tools |
MF | Abstract class for matrix factorization based item predictors |
Minimum | Group recommender that takes the minimum user score as the group score |
Model | Class containing static routines for reading and writing recommender models |
MostPopular | Most-popular item recommender |
MostPopularByAttributes | Recommend most popular items by attribute |
MostPopularByNominalAttributes | Recommend most popular items by attribute |
MovieLensRatingData | Class that offers static methods for reading in MovieLens 1M and 10M rating data |
MultiCore | Class containing utility routines for multi-core algorithms |
MultiCoreBPRMF | Matrix factorization for BPR on multiple cores |
NaiveBayes | Attribute-aware rating predictor using Naive Bayes |
NDCG | Normalized discounted cumulative gain (NDCG) of a list of ranked items |
NelderMead | Nealder-Mead algorithm for finding suitable hyperparameters |
NominalAttribute | |
Overlap | Class containing routines for computing overlaps |
PairwiseWins | A simple Condorcet-style voting mechanism |
Pearson | Shrunk Pearson correlation for rating data |
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 |
Random | Uses a random rating value for prediction |
Random | Random item recommender for use as experimental baseline |
Random | Random number generator singleton class |
RatingCrossValidationSplit | K-fold cross-validation split for rating prediction |
RatingData | Class that offers methods for reading in rating data |
RatingPredictionEvaluationResults | Rating prediction evaluation results |
RatingPredictor | Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction) |
Ratings | Evaluation class for rating prediction |
Ratings | Data structure for storing ratings |
Ratings | Class that offers static methods for reading in rating data from the KDD Cup 2011 files |
RatingScale | Class containing information about the rating scale of a data set: valid rating values, minimum/maximum rating |
RatingsChronologicalSplit | Chronological split for rating prediction |
RatingsCrossValidation | Cross-validation for rating prediction |
RatingsOnline | Online evaluation for rating prediction |
RatingsPerUserChronologicalSplit | Per-user chronological split for rating prediction |
RatingsProxy | Data structure that allows access to selected entries of a rating data structure |
RatingsSimpleSplit | Simple split for rating prediction |
ReciprocalRank | The reciprocal rank of a list of ranked items |
Recommender | Abstract recommender class implementing default behaviors |
RecommenderParameters | Class for key-value pair string processing |
RelationData | Class that offers static methods to read (binary) relation over entities into IBooleanMatrix objects |
RMSE | Utility functions for the root mean square error (RMSE) |
SequentialDiversification | Sequential diversification |
SigmoidCombinedAsymmetricFactorModel | Asymmetric factor model which represents items in terms of the users that rated them, and users in terms of the items they rated |
SigmoidItemAsymmetricFactorModel | Asymmetric factor model |
SigmoidSVDPlusPlus | SVD++: Matrix factorization that also takes into account _what_ users have rated; variant that uses a sigmoid function |
SigmoidUserAsymmetricFactorModel | Asymmetric factor model which represents items in terms of the users that rated them |
SkewSymmetricSparseMatrix | Skew symmetric (anti-symmetric) sparse matrix; consumes less memory |
SlopeOne | Frequency-weighted Slope-One rating prediction |
SocialMF | Social-network-aware matrix factorization |
SoftMarginRankingMF | Matrix factorization model for item prediction optimized for a soft margin (hinge) ranking loss, using stochastic gradient descent (as in BPR-MF) |
SparseBooleanMatrix | Sparse representation of a boolean matrix, using HashSets |
SparseMatrix< T > | Class for storing sparse matrices |
SparseMatrixExtensions | Utilities to work with matrices |
StaticByteRatings | Array-based storage for rating data |
StaticRatingData | Class that offers methods for reading in static rating data |
StaticRatings | Array-based storage for rating data |
SVDPlusPlus | SVD++: Matrix factorization that also takes into account _what_ users have rated |
SymmetricCorrelationMatrix | Class for computing and storing correlations and similarities |
SymmetricMatrix< T > | Class for storing dense matrices |
SymmetricSparseMatrix< T > | Symmetric sparse matrix; consumes less memory |
TimeAwareBaseline | Time-aware bias model |
TimeAwareBaselineWithFrequencies | Time-aware bias model with frequencies |
TimeAwareRatingPredictor | Abstract class for time-aware rating predictors |
TimedRatingData | Class that offers methods for reading in rating data with time information |
TimedRatings | Data structure for storing ratings with time information |
TimedRatingsProxy | Data structure that allows access to selected entries of a timed rating data structure |
Track2Items | Class that offers static methods for reading in test data from the KDD Cup 2011 files |
TransductiveRatingPredictorExtensions | Helper methods for ITransductiveRatingPredictor |
UserAttributeKNN | K-nearest neighbor (kNN) user-based collaborative filtering using the correlation of 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 | K-nearest neighbor user-based collaborative filtering |
UserKNN | Weighted user-based kNN |
Utils | Class containing utility functions |
VectorExtensions | Extensions for vector-like data |
VectorExtensions | Extensions for vector-like data |
WeightedAverage | Group recommender that averages user scores weighted by the rating frequency of the individual users |
WeightedBPRMF | Weigthed BPR-MF with frequency-adjusted sampling |
WeightedEnsemble | Combining several predictors with a weighted ensemble |
Wrap | Static methods to wrap around other code |
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 |