All Classes

Class	Description
AdjustedCosine	Implements traditional Adjusted Cosine as CF similarity metric for the items.
AdjustedCosine	Implements traditional Adjusted Cosine as CF similarity metric.
BeMF	Implements Ortega, F., Lara-Cabrera, R., González-Prieto, Á., & Bobadilla, J. (2021).
BenchmarkDataModels	This class allows final users to work with benchmark DataModel instances.
BiasedMF	Implements Koren, Y., Bell, R., & Volinsky, C. (2009).
BiasedMFGridSearch	In this example we tune the hyper-parameters of BiasedMF recommender using the GridSearch tool.
BNMF	Implements Hernando, A., Bobadilla, J., & Ortega, F. (2016).
CJMSD	Implements the following CF similarity metric: Bobadilla, J., Ortega, F., Hernando, A., & Arroyo, A. (2012).
CLiMF	Implements Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., & Hanjalic, A
ColumnPlot	Implements a column plot.
ColumnPlotExample	In this example we analyze the rating value distribution of MovieLens 1M dataset using a ColumnPlot.
Correlation	This class Implements Pearson Correlation as CF similarity metric for the items.
Correlation	Implements traditional Pearson Correlation as CF similarity metric.
CorrelationConstrained	This class implements the Constrained Correlation as CF similarity metric for items.
CorrelationConstrained	Implements traditional Pearson Correlation Constrained as CF similarity metric.
Cosine	Implements Cosine as CF similarity metric for the items.
Cosine	Implements traditional Cosine as CF similarity metric.
Coverage	This class calculates the Coverage of the recommender system.
DataBank	DataBank is focused on storing heterogeneous data as a support to the calculations made in the recommendation process.
DataModel	This class manages all the information related with a collaborative filtering based recommender system.
DataSet	This interface works as a bridge between the raw file and the DataModel.
DataSetEntry	Structural class which stores Triplets: user identifier (String), item identifier (string), and rating value.
DeepMF	Implements Lara-Cabrera, R., González-Prieto, Á., & Ortega, F. (2020).
DirMF	Implements Lara-Cabrera, R., González, Á., Ortega, F., & González-Prieto, Á. (2022).
Discovery	This class the averaged novelty of the recomendations.
Diversity	This class the averaged diversity of the recomendations.
F1	This class calculates the F1 score of the recommender system.
GettingStartedExample	In this example we analyze how the Mean Squared Error (MSE) varies according to the value of the regularization term in Probabilistic Matrix Factorization (PMF).
GMF	Implements He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.
GridSearch	Utility class to performs a grid search over a Recommender instance.
GridSearchCV	Utility class to performs a grid search over a Recommender instance.
HistogramPlot	Implements a histogram plot.
HistogramPlotExample	In this example we analyze the average rating of each item that belongs to MovieLens 1M dataset.
HPF	Implements Gopalan, P., Hofman, J.
Item	Defines a composition of an Item.
ItemKNN	Implements item-to-item KNN based collaborative filtering
ItemKNN.AggregationApproach	Available aggregation approaches to merge k-nearest neighbors ratings
ItemKnnComparison	In this example we compare the MSLE and nDCG quality measures scores for different similarity metrics applied to item-to-item knn based collaborative filtering.
ItemSimilarityMetric	This class process the similarity measure between two items.
Jaccard	This class Implements Jaccard Index as CF similarity metric for the items.
Jaccard	Implements traditional Jaccard Index as CF similarity metric.
JMSD	This class implements JMSD as the similarity metric for the items.
JMSD	Implements the following CF similarity metric: Bobadilla, J., Serradilla, F., & Bernal, J
LinePlot	Implements a LinePlot.
LinePlotExample	In this example we compare the F1 score of the recommendations performed by PMF and NMF recommenders.
MAE	This class calculates the Mean Absolute Error (MAE) between the predictions and the test ratings.
ManualDataSet
Maths	This class contains useful math methods.
MatrixFactorizationComparison	In this example we compare the RMSE score for different matrix factorization models varying the number of latent factors.
Max	This class calculates the averaged maximum prediction absolute error in a the prediction of a test rating for each test user.
MLP	Implements He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.
MSD	Implements traditional MSD as CF similarity metric for items.
MSD	Implements traditional MSD as CF similarity metric.
MSE	This class calculates the Mean Squared Error (MSE) between the predictions and the test ratings.
MSLE	This class calculates the Mean Squared Logarithmic Error (MSLE) between the predictions and the test ratings.
NCCF	Implements short method of Bobadilla, J., Ortega, F., Gutiérrez, A., & Alonso, S. (2020).
NDCG	This class calculates the Normalized Discounted Cumulative Gain (nDCG) of the recommendations performed by a Recommender.
NeuMF	Implements He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.
NMF	Implements Lee, D.
Novelty	This class the averaged novelty of the recomendations.
Parallelizer	This class is used to simplify the parallelization of collaborative filtering algorithms
ParamsGrid	This class generates the development set for a grid search.
Partible<T>	This interface handles the parallel execution of an array of T thorough Parallelizer class.
Perfect	This class calculates the percentage of perfect predictions.
PIP	This class implements the PIP CF similarity metric for the items.
PIP	Implements the following CF similarity metric: Ahn, H.
Plot	Abstract class that represents a CF4J plot.
PlotSettings	This class contains global plot settings.
PMF	Implements Mnih, A., & Salakhutdinov, R.
PMFRandomSearchCV	In this example we tune the hyper-parameters of PMF recommender using the RandomSearchCV tool.
Precision	This class calculates the precision of the recommendations performed by a Recommender.
QualityMeasure	Abstract class used to simplify the evaluation of collaborative filtering based recommendation models.
R2	This class calculates the the coefficient of determination, usually denoted as R2, of the predictions performed by a recommender.
RandomSearch	Utility class to performs a random search over a Recommender instance.
RandomSearchCV	Utility class to performs a random search over a Recommender instance.
RandomSplitDataSet	This class implements the DataSet interface by random splitting the collaborative filtering ratings allocated in a text file.
Rating	The class Rating is an structure of a pair of elements, which are the index and the rating.
Recall	This class calculates the recall of the recommendations performed by a Recommender.
Recommender	Abstract class that represents any recommender.
RMSE	This class calculates the Root Mean Squared Error (RMSE) between the predictions and the test ratings.
ScatterPlot	Implements an ScatterPlot.
ScatterPlotExample	In this example we build an ScatterPlot comparing the number of ratings of each test user with his/her averaged prediction error using BiasedMF as recommender.
Search	This class contains useful search methods.
Singularities	This class implements the singularities CF similarity metric.
Singularities	Implements the following CF similarity metric: Bobadilla, J., Ortega, F., & Hernando, A
SortedRatingList	SortedRatingList is a specific type of sorted ArrayList that uses the Rating class internally.
SpearmanRank	Implements traditional Spearman Rank as CF similarity metric for the items.
SpearmanRank	Implements traditional Spearman Rank as CF similarity metric.
SVDPlusPlus	Implements Koren, Y. (2008, August).
TestItem	A TestItem extends an Item given it the following properties: Index in the DataModel array which stores test items.
TestUser	A TestUser extends an User given him or her the following properties: Index in the DataModel array which stores test users.
TrainTestFilesDataSet	This class implements the DataSet interface by loading training and test ratings from separated text files.
URP	Implements Marlin, B.
User	Defines a composition of an User.
UserKNN	Implements user-to-user KNN based collaborative filtering
UserKNN.AggregationApproach	Available aggregation approaches to merge k-nearest neighbors ratings
UserKnnComparison	In this example we compare the MAE, Coverage, Precision and Recall quality measures scores for different similarity metrics applied to user-to-user knn based collaborative filtering.
UserKNNGridSearch	In this example we tune the parameters of UserKNN recommender using the GridSearch tool.
UserSimilarityMetric	This class process the similarity measure between two users.
XYPlot	Implements an XYPlot.
XYPlotExample	In this example we compare the Precision score (y axis) and the Recall score (x axis) for PMF and NMF recommenders using an XYPlot.