A B C D E F G I L M N O P R S T U V W 

A

AbstractErrorMetric - Class in net.recommenders.rival.evaluation.metric.error
 
AbstractErrorMetric(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.error.AbstractErrorMetric
Default constructor with predictions and groundtruth information
AbstractErrorMetric(DataModel<Long, Long>, DataModel<Long, Long>, AbstractErrorMetric.ErrorStrategy) - Constructor for class net.recommenders.rival.evaluation.metric.error.AbstractErrorMetric
Constructor where the error strategy can be initialized
AbstractErrorMetric.ErrorStrategy - Enum in net.recommenders.rival.evaluation.metric.error
Type of error strategy: what to do when there is no predicted rating but there is groundtruth information
AbstractLastfmCelmaParser - Class in net.recommenders.rival.split.parser
Parser for the Last.fm dataset by O.
AbstractLastfmCelmaParser(boolean) - Constructor for class net.recommenders.rival.split.parser.AbstractLastfmCelmaParser
Default constructor.
AbstractMetric - Class in net.recommenders.rival.evaluation.metric
 
AbstractMetric(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.AbstractMetric
Default constructor with predictions and groundtruth information
AbstractRankingMetric - Class in net.recommenders.rival.evaluation.metric.ranking
Normalized discounted cumulative gain (NDCG) of a ranked list of items.
AbstractRankingMetric(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.AbstractRankingMetric
Default constructor with predictions and groundtruth information
AbstractRankingMetric(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.AbstractRankingMetric
Constructor where the relevance threshold can be initialized
AbstractRankingMetric(DataModel<Long, Long>, DataModel<Long, Long>, double, int[]) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.AbstractRankingMetric
Constructor where the cutoff levels can be initialized
AbstractRunner - Class in net.recommenders.rival.recommend.frameworks
 
AbstractRunner(Properties) - Constructor for class net.recommenders.rival.recommend.frameworks.AbstractRunner
Default constructor.
AbstractStrategy - Class in net.recommenders.rival.evaluation.strategy
A basic evaluation strategy.
AbstractStrategy(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.strategy.AbstractStrategy
Default constructor for the evaluation strategy.
addPreference(U, I, Double) - Method in class net.recommenders.rival.core.DataModel
Method that adds a preference to the model between a user and an item.
addTimestamp(U, I, Long) - Method in class net.recommenders.rival.core.DataModel
Method that adds a timestamp to the model between a user and an item.
AllItems - Class in net.recommenders.rival.evaluation.strategy
An evaluation strategy where all the items are used as candidates.
AllItems(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.strategy.AllItems
 
ARTIST_TOK - Static variable in class net.recommenders.rival.split.parser.LastfmCelma1KParser
The column index for the artist id in the file.
ARTIST_TOK - Static variable in class net.recommenders.rival.split.parser.LastfmCelma360KParser
The column index for the artist id in the file.

B

buildRecommender(DataModel) - Method in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
 
buildRecommender(DataModel, String) - Method in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
Default CF recommender.
buildRecommender(DataModel, String, String) - Method in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
Recommender based on given recType and simType
buildRecommender(DataModel, String, String, int) - Method in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
Recommender based on given recType, simType and neighborhood type
buildRecommender(DataModel, String, String, int, int) - Method in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
SVD
buildRecommender(DataModel, String, String, int, int, int, String) - Method in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
 

C

clear() - Method in class net.recommenders.rival.core.DataModel
Method that clears all the maps contained in the model.
compute() - Method in class net.recommenders.rival.evaluation.metric.error.MAE
Instantiates and computes the MAE value.
compute() - Method in class net.recommenders.rival.evaluation.metric.error.RMSE
Instantiates and computes the RMSE value.
compute() - Method in interface net.recommenders.rival.evaluation.metric.EvaluationMetric
Computes the evaluation metric.
compute() - Method in class net.recommenders.rival.evaluation.metric.ranking.MAP
Computes the global MAP by first summing the AP (average precision) for each user and then averaging by the number of users.
compute() - Method in class net.recommenders.rival.evaluation.metric.ranking.NDCG
Computes the global NDCG by first summing the NDCG for each user and then averaging by the number of users.
compute() - Method in class net.recommenders.rival.evaluation.metric.ranking.Precision
Computes the global precision by first summing the precision for each user and then averaging by the number of users.
compute() - Method in class net.recommenders.rival.evaluation.metric.ranking.Recall
Computes the global recall by first summing the recall for each user and then averaging by the number of users.
considerEstimatedPreference(AbstractErrorMetric.ErrorStrategy, double) - Static method in class net.recommenders.rival.evaluation.metric.error.AbstractErrorMetric
Method that returns an estimated preference according to a given value and an error strategy
CrossValidationSplitter - Class in net.recommenders.rival.split.splitter
Class that splits a dataset using a cross validation technique (every interaction in the data only appears once in each test split).
CrossValidationSplitter(int, boolean, long) - Constructor for class net.recommenders.rival.split.splitter.CrossValidationSplitter
Constructor

D

DataModel<U,I> - Class in net.recommenders.rival.core
Data model used throughout the toolkit.
DataModel() - Constructor for class net.recommenders.rival.core.DataModel
Default constructor
DataModel(Map<U, Map<I, Double>>, Map<I, Map<U, Double>>, Map<U, Map<I, Set<Long>>>) - Constructor for class net.recommenders.rival.core.DataModel
Constructor with parameters.
DATASET_FILE - Static variable in class net.recommenders.rival.split.parser.ParserRunner
Variables that represent the name of several properties in the file.
DATASET_PARSER - Static variable in class net.recommenders.rival.split.parser.ParserRunner
 
DATASET_SPLITTER - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
Variables that represent the name of several properties in the file.
DEFAULT_ITERATIONS - Static variable in class net.recommenders.rival.recommend.frameworks.AbstractRunner
Default number of iterations.
DEFAULT_ITERATIONS - Static variable in class net.recommenders.rival.recommend.frameworks.mahout.MahoutRecommenderRunner
Default number of iterations.
DEFAULT_N - Static variable in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
Number of neighbors.
DEFAULT_NEIGHBORHOOD_SIZE - Static variable in class net.recommenders.rival.recommend.frameworks.mahout.MahoutRecommenderRunner
Default neighborhood size.

E

ERROR_STRATEGY - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
ERROR_STRATEGY - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
estimatePreference(long, long) - Method in class net.recommenders.rival.recommend.frameworks.mahout.PopularityBasedRecommender
 
EvaluationMetric<V> - Interface in net.recommenders.rival.evaluation.metric
An evaluation metric expressing the quality of the evaluated system.
EvaluationMetricRunner - Class in net.recommenders.rival.evaluation.metric
Runner for a single evaluation metric.
EvaluationMetricRunner() - Constructor for class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
EvaluationStrategy<U,I> - Interface in net.recommenders.rival.evaluation.strategy
An interface for evaluation strategies.
EvaluationStrategy.OUTPUT_FORMAT - Enum in net.recommenders.rival.evaluation.strategy
Enumeration that defines two output formats: a simple one (tab-separated) and another compatible with the one used by the treceval program.
EvaluationStrategy.Pair<A,B> - Class in net.recommenders.rival.evaluation.strategy
Bean class to store an element of type A and another of type B
EvaluationStrategy.Pair(A, B) - Constructor for class net.recommenders.rival.evaluation.strategy.EvaluationStrategy.Pair
 
EvaluationStrategyPerItem<U,I> - Interface in net.recommenders.rival.evaluation.strategy
An interface for the per-item evaluation strategy.

F

factorizer - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the factorizer
factors - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the factors
framework - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the framework

G

generateOutput(DataModel<Long, Long>, int[], EvaluationMetric<Long>, String, Boolean, File, Boolean, Boolean) - Static method in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
Generates the output of the evaluation.
generateOutput(DataModel<Long, Long>, Map<Long, List<EvaluationStrategy.Pair<Long, Double>>>, EvaluationStrategy<Long, Long>, EvaluationStrategy.OUTPUT_FORMAT, File, File, String, String, String, String, Boolean) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
Runs a particular strategy on some data and outputs the result into a file.
generateOutput(DataModel<Long, Long>, File, EvaluationStrategy<Long, Long>, EvaluationStrategy.OUTPUT_FORMAT, File, File, String, String, String, String, Boolean) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
Runs a particular strategy on some data and outputs the result into a file.
generateOutput(DataModel<Long, Long>, Map<Long, List<EvaluationStrategy.Pair<Long, Double>>>, EvaluationStrategy<Long, Long>, EvaluationStrategy.OUTPUT_FORMAT, File, File, Boolean) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunner
Generates the output of the evaluation.
generateOutput(DataModel<Long, Long>, File, EvaluationStrategy<Long, Long>, EvaluationStrategy.OUTPUT_FORMAT, File, File, Boolean) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
Runs a particular strategy on some data using pre-computed recommendations and outputs the result into a file.
GenericRecommenderBuilder<T> - Class in net.recommenders.rival.recommend.frameworks.mahout
 
GenericRecommenderBuilder() - Constructor for class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
 
getAllPredictionFiles(Set<String>, File, String) - Static method in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
Get all prediction files.
getAllRecommendationFiles(Set<String>, File, String, String) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
Get all recommendation files.
getAllRecommendationFiles(Set<String>, File, String, String) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
Get all recommendation files.
getAllSplits(Set<String>, File, String, String) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
Get all training/test splits.
getAllSplits(Set<String>, File, String, String) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
Get all training/test splits.
getAlreadyRecommended() - Method in class net.recommenders.rival.recommend.frameworks.AbstractRunner
Check if there already exist recommendations for this recommender.
getCandidateItemsToRank(Long) - Method in class net.recommenders.rival.evaluation.strategy.AllItems
 
getCandidateItemsToRank(U) - Method in interface net.recommenders.rival.evaluation.strategy.EvaluationStrategy
Get the items to rank.
getCandidateItemsToRank(U, I) - Method in interface net.recommenders.rival.evaluation.strategy.EvaluationStrategyPerItem
Get the items to rank.
getCandidateItemsToRank(Long) - Method in class net.recommenders.rival.evaluation.strategy.RelPlusN
 
getCandidateItemsToRank(Long) - Method in class net.recommenders.rival.evaluation.strategy.TestItems
 
getCandidateItemsToRank(Long) - Method in class net.recommenders.rival.evaluation.strategy.TrainItems
 
getCandidateItemsToRank(Long) - Method in class net.recommenders.rival.evaluation.strategy.UserTest
 
getCanonicalFileName() - Method in class net.recommenders.rival.recommend.frameworks.AbstractRunner
Get file name with canonical path.
getFirst() - Method in class net.recommenders.rival.evaluation.strategy.EvaluationStrategy.Pair
 
getIndexMap(File, Map<String, Long>) - Static method in class net.recommenders.rival.split.parser.AbstractLastfmCelmaParser
Read a user/item mapping (user/item original value, user/item internal id) from a file and return the maximum index number in that file.
getItems() - Method in class net.recommenders.rival.core.DataModel
Method that returns the items in the model.
getItemUserPreferences() - Method in class net.recommenders.rival.core.DataModel
Method that returns the preference map between items and users.
getNumItems() - Method in class net.recommenders.rival.core.DataModel
Method that returns the number of items in the model.
getNumUsers() - Method in class net.recommenders.rival.core.DataModel
Method that returns the number of users in the model.
getSecond() - Method in class net.recommenders.rival.evaluation.strategy.EvaluationStrategy.Pair
 
getUserItemPreferences() - Method in class net.recommenders.rival.core.DataModel
Method that returns the preference map between users and items.
getUserItemTimestamps() - Method in class net.recommenders.rival.core.DataModel
Method that returns the map with the timestamps between users and items.
getUsers() - Method in class net.recommenders.rival.core.DataModel
Method that returns the users in the model.
getValue(Long) - Method in class net.recommenders.rival.evaluation.metric.AbstractMetric
 
getValue() - Method in class net.recommenders.rival.evaluation.metric.error.AbstractErrorMetric
 
getValue() - Method in interface net.recommenders.rival.evaluation.metric.EvaluationMetric
Get the overall value of the metric.
getValue(V) - Method in interface net.recommenders.rival.evaluation.metric.EvaluationMetric
Get the value of the metric for a specific user.
getValue() - Method in class net.recommenders.rival.evaluation.metric.ranking.AbstractRankingMetric
 
getValueAt(int) - Method in class net.recommenders.rival.evaluation.metric.ranking.AbstractRankingMetric
Method to return the metric value at a particular cutoff level.
getValueAt(long, int) - Method in class net.recommenders.rival.evaluation.metric.ranking.AbstractRankingMetric
Method to return the metric value at a particular cutoff level for a given user.
getValueAt(int) - Method in class net.recommenders.rival.evaluation.metric.ranking.MAP
Method to return the MAP value at a particular cutoff level.
getValueAt(long, int) - Method in class net.recommenders.rival.evaluation.metric.ranking.MAP
Method to return the AP (average precision) value at a particular cutoff level for a given user.
getValueAt(int) - Method in class net.recommenders.rival.evaluation.metric.ranking.NDCG
Method to return the NDCG value at a particular cutoff level.
getValueAt(long, int) - Method in class net.recommenders.rival.evaluation.metric.ranking.NDCG
Method to return the NDCG value at a particular cutoff level for a given user.
getValueAt(int) - Method in class net.recommenders.rival.evaluation.metric.ranking.Precision
Method to return the precision value at a particular cutoff level.
getValueAt(long, int) - Method in class net.recommenders.rival.evaluation.metric.ranking.Precision
Method to return the precision value at a particular cutoff level for a given user.
getValueAt(int) - Method in class net.recommenders.rival.evaluation.metric.ranking.Recall
Method to return the recall value at a particular cutoff level.
getValueAt(long, int) - Method in class net.recommenders.rival.evaluation.metric.ranking.Recall
Method to return the recall value at a particular cutoff level for a given user.
getValuePerUser() - Method in class net.recommenders.rival.evaluation.metric.AbstractMetric
 
getValuePerUser() - Method in interface net.recommenders.rival.evaluation.metric.EvaluationMetric
Get the value of the metric on a per-user basis.
GROUNDTRUTH_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
GROUNDTRUTH_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
GROUNDTRUTH_FOLDER - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
GROUNDTRUTH_FOLDER - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 

I

INPUT - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
INPUT_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
INPUT_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
ITEM_TOK - Static variable in class net.recommenders.rival.core.SimpleParser
The column index for the item id in the file.
ITEM_TOK - Static variable in class net.recommenders.rival.evaluation.parser.TrecEvalParser
The column index for the item id in the file.
ITEM_TOK - Static variable in class net.recommenders.rival.split.parser.MovielensParser
The column index for the item id in the file.
iterations - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the iterations

L

LASTFM_IDS_PREFIX - Static variable in class net.recommenders.rival.split.parser.ParserRunner
 
LASTFM_USEARTISTS - Static variable in class net.recommenders.rival.split.parser.ParserRunner
 
LastfmCelma1KParser - Class in net.recommenders.rival.split.parser
 
LastfmCelma1KParser(boolean) - Constructor for class net.recommenders.rival.split.parser.LastfmCelma1KParser
 
LastfmCelma360KParser - Class in net.recommenders.rival.split.parser
 
LastfmCelma360KParser(boolean) - Constructor for class net.recommenders.rival.split.parser.LastfmCelma360KParser
 
LENSKIT - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for LensKit
LENSKIT_ITEMBASED_RECS - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
LENSKIT_SIMILARITIES - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
LENSKIT_SVD_RECS - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
LENSKIT_USERBASED_RECS - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
LenskitRecommenderRunner - Class in net.recommenders.rival.recommend.frameworks.lenskit
 
LenskitRecommenderRunner(Properties) - Constructor for class net.recommenders.rival.recommend.frameworks.lenskit.LenskitRecommenderRunner
Default constructor
listAllFiles(Set<String>, String) - Static method in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 

M

MAE - Class in net.recommenders.rival.evaluation.metric.error
Mean absolute error (MAE) of a list of predicted ratings.
MAE(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.error.MAE
Default constructor with predictions and groundtruth information
MAE(DataModel<Long, Long>, DataModel<Long, Long>, AbstractErrorMetric.ErrorStrategy) - Constructor for class net.recommenders.rival.evaluation.metric.error.MAE
Constructor where the error strategy can be initialized
MAHOUT - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for Mahout
MAHOUT_ITEMBASED_RECS - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
MAHOUT_SIMILARITIES - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
MAHOUT_SVD_FACTORIZER - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
MAHOUT_SVD_RECS - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
MAHOUT_USERBASED_RECS - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
MahoutRecommenderRunner - Class in net.recommenders.rival.recommend.frameworks.mahout
 
MahoutRecommenderRunner(Properties) - Constructor for class net.recommenders.rival.recommend.frameworks.mahout.MahoutRecommenderRunner
Default constructor.
main(String[]) - Static method in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
Main method for running a single evaluation metric.
main(String[]) - Static method in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
Main method for running a single evaluation metric.
main(String[]) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
Main method.
main(String[]) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
Main method.
main(String[]) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunner
Main method for running a single evaluation strategy.
main(String[]) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
Main function.
main(String[]) - Static method in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
main(String[]) - Static method in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
Main method for running a recommendation.
main(String[]) - Static method in class net.recommenders.rival.split.splitter.Split
 
MAP - Class in net.recommenders.rival.evaluation.metric.ranking
Mean Average Precision of a ranked list of items.
MAP(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.MAP
Default constructor with predictions and groundtruth information
MAP(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.MAP
Constructor where the relevance threshold can be initialized
MAP(DataModel<Long, Long>, DataModel<Long, Long>, double, int[]) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.MAP
Constructor where the cutoff levels can be initialized
METRIC - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
METRIC_PER_USER - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
METRIC_PER_USER - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
METRICS - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
MovielensParser - Class in net.recommenders.rival.split.parser
A parser based on the format of Movielens files
MovielensParser() - Constructor for class net.recommenders.rival.split.parser.MovielensParser
 
MultipleEvaluationMetricRunner - Class in net.recommenders.rival.evaluation.metric
Runner for multiple evaluation metrics.
MultipleEvaluationMetricRunner() - Constructor for class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
MultipleRecommendationRunner - Class in net.recommenders.rival.recommend.frameworks
 
MultipleRecommendationRunner() - Constructor for class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
MultipleStrategyRunner - Class in net.recommenders.rival.evaluation.strategy
Runner of multiple evaluation strategies.
MultipleStrategyRunner() - Constructor for class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
MultipleStrategyRunnerInfile - Class in net.recommenders.rival.evaluation.strategy
Runner of multiple evaluation strategies using StrategyRunnerInfile.
MultipleStrategyRunnerInfile() - Constructor for class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 

N

N - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
NDCG - Class in net.recommenders.rival.evaluation.metric.ranking
Normalized discounted cumulative gain (NDCG) of a ranked list of items.
NDCG(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.NDCG
Default constructor with predictions and groundtruth information
NDCG(DataModel<Long, Long>, DataModel<Long, Long>, int[]) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.NDCG
Constructor where the cutoff levels can be initialized
NDCG(DataModel<Long, Long>, DataModel<Long, Long>, double, int[], NDCG.TYPE) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.NDCG
Constructor where the cutoff levels and the type of NDCG computation can be initialized
NDCG.TYPE - Enum in net.recommenders.rival.evaluation.metric.ranking
Type of nDCG computation (linear or exponential)
NDCG_TYPE - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
NDCG_TYPE - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
neighborhood - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the neighborhood
net.recommenders.rival.core - package net.recommenders.rival.core
 
net.recommenders.rival.evaluation.metric - package net.recommenders.rival.evaluation.metric
 
net.recommenders.rival.evaluation.metric.error - package net.recommenders.rival.evaluation.metric.error
 
net.recommenders.rival.evaluation.metric.ranking - package net.recommenders.rival.evaluation.metric.ranking
 
net.recommenders.rival.evaluation.parser - package net.recommenders.rival.evaluation.parser
 
net.recommenders.rival.evaluation.strategy - package net.recommenders.rival.evaluation.strategy
 
net.recommenders.rival.recommend.frameworks - package net.recommenders.rival.recommend.frameworks
 
net.recommenders.rival.recommend.frameworks.lenskit - package net.recommenders.rival.recommend.frameworks.lenskit
 
net.recommenders.rival.recommend.frameworks.mahout - package net.recommenders.rival.recommend.frameworks.mahout
 
net.recommenders.rival.recommend.frameworks.mahout.exceptions - package net.recommenders.rival.recommend.frameworks.mahout.exceptions
 
net.recommenders.rival.split.parser - package net.recommenders.rival.split.parser
 
net.recommenders.rival.split.splitter - package net.recommenders.rival.split.splitter
 
NO_N - Static variable in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
Number of neighbors when no neighbors are to be used.
NOFACTORS - Static variable in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
No factors.
NOITER - Static variable in class net.recommenders.rival.recommend.frameworks.mahout.GenericRecommenderBuilder
No iterations.

O

OUTPUT - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
output - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the output
OUTPUT_APPEND - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
OUTPUT_APPEND - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
OUTPUT_FILE - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
OUTPUT_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
OUTPUT_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
OUTPUT_FOLDER - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
OUTPUT_FOLDER - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
OUTPUT_FOLDER - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
OUTPUT_FORMAT - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
OUTPUT_FORMAT - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
OUTPUT_FORMAT - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
OUTPUT_FORMAT - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
OUTPUT_OVERWRITE - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
OUTPUT_OVERWRITE - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
OUTPUT_OVERWRITE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
OUTPUT_OVERWRITE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 

P

parseData(File) - Method in interface net.recommenders.rival.core.Parser
Parse data file.
parseData(File, String) - Method in interface net.recommenders.rival.core.ParserWithIdMapping
Parse data file
parseData(File) - Method in class net.recommenders.rival.core.SimpleParser
 
parseData(File, String) - Method in class net.recommenders.rival.core.SimpleParser
 
parseData(File) - Method in class net.recommenders.rival.evaluation.parser.TrecEvalParser
 
parseData(File, String) - Method in class net.recommenders.rival.split.parser.LastfmCelma1KParser
 
parseData(File, String) - Method in class net.recommenders.rival.split.parser.LastfmCelma360KParser
 
parseData(File) - Method in class net.recommenders.rival.split.parser.MovielensParser
 
Parser - Interface in net.recommenders.rival.core
Data model parser interface.
ParserRunner - Class in net.recommenders.rival.split.parser
Runner for the parser classes.
ParserRunner() - Constructor for class net.recommenders.rival.split.parser.ParserRunner
 
ParserWithIdMapping - Interface in net.recommenders.rival.core
Parser of files where users or items are not represented as integer ids
PopularityBasedRecommender - Class in net.recommenders.rival.recommend.frameworks.mahout
 
PopularityBasedRecommender(DataModel, CandidateItemsStrategy) - Constructor for class net.recommenders.rival.recommend.frameworks.mahout.PopularityBasedRecommender
Constructor when a canidate item strategy is to be used.
PopularityBasedRecommender(DataModel) - Constructor for class net.recommenders.rival.recommend.frameworks.mahout.PopularityBasedRecommender
Default constructor.
Precision - Class in net.recommenders.rival.evaluation.metric.ranking
Precision of a ranked list of items.
Precision(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.Precision
Default constructor with predictions and groundtruth information
Precision(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.Precision
Constructor where the relevance threshold can be initialized
Precision(DataModel<Long, Long>, DataModel<Long, Long>, double, int[]) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.Precision
Constructor where the cutoff levels can be initialized
PREDICTION_FILE - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
Variables that represent the name of several properties in the file.
PREDICTION_FILE_FORMAT - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
PREDICTION_FILE_FORMAT - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
PREDICTION_FOLDER - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
Variables that represent the name of several properties in the file.
PREDICTION_PREFIX - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
PREF_TOK - Static variable in class net.recommenders.rival.split.parser.LastfmCelma360KParser
The column index for the preference number in the file.
printGroundtruth(Long, PrintStream, EvaluationStrategy.OUTPUT_FORMAT) - Method in class net.recommenders.rival.evaluation.strategy.AbstractStrategy
 
printGroundtruth(U, PrintStream, EvaluationStrategy.OUTPUT_FORMAT) - Method in interface net.recommenders.rival.evaluation.strategy.EvaluationStrategy
Print the ground truth.
printGroundtruth(Long, PrintStream, EvaluationStrategy.OUTPUT_FORMAT) - Method in class net.recommenders.rival.evaluation.strategy.RelPlusN
 
printRanking(Long, List<EvaluationStrategy.Pair<Long, Double>>, PrintStream, EvaluationStrategy.OUTPUT_FORMAT) - Method in class net.recommenders.rival.evaluation.strategy.AbstractStrategy
 
printRanking(U, List<EvaluationStrategy.Pair<I, Double>>, PrintStream, EvaluationStrategy.OUTPUT_FORMAT) - Method in interface net.recommenders.rival.evaluation.strategy.EvaluationStrategy
Print rankings for a user.
printRanking(Long, List<EvaluationStrategy.Pair<Long, Double>>, PrintStream, EvaluationStrategy.OUTPUT_FORMAT) - Method in class net.recommenders.rival.evaluation.strategy.RelPlusN
 
processDataAsPredictedDifferencesToTest() - Method in class net.recommenders.rival.evaluation.metric.error.AbstractErrorMetric
Method that transforms the user data from pairs of (item, score) into lists of differences, by using groundtruth information.
processDataAsRankedTestRelevance() - Method in class net.recommenders.rival.evaluation.metric.ranking.AbstractRankingMetric
Method that transforms the user data from pairs of (item, score) into ranked lists of relevance values, by using ground truth information.
properties - Variable in class net.recommenders.rival.recommend.frameworks.AbstractRunner
The properties.

R

RandomSplitter - Class in net.recommenders.rival.split.splitter
Class that splits a dataset randomly.
RandomSplitter(float, boolean, long, boolean) - Constructor for class net.recommenders.rival.split.splitter.RandomSplitter
Constructor
RANKING_CUTOFFS - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
RANKING_CUTOFFS - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
RATING_TOK - Static variable in class net.recommenders.rival.core.SimpleParser
The column index for the rating in the file.
RATING_TOK - Static variable in class net.recommenders.rival.evaluation.parser.TrecEvalParser
The column index for the rating in the file.
RATING_TOK - Static variable in class net.recommenders.rival.split.parser.MovielensParser
The column index for the rating in the file.
readLine(String, Map<Long, List<EvaluationStrategy.Pair<Long, Double>>>) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunner
Read a file from the recommended items file.
readLine(String, Map<Long, List<EvaluationStrategy.Pair<Long, Double>>>) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
Method that reads a line that contains a(some) recommendation(s) and store it in a map.
readScoredItems(File, Long) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
Method that reads the scores given to items by a recommender only for a given user (it ignores the rest).
Recall - Class in net.recommenders.rival.evaluation.metric.ranking
Recall of a ranked list of items.
Recall(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.Recall
Default constructor with predictions and groundtruth information
Recall(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.Recall
Constructor where the relevance threshold can be initialized
Recall(DataModel<Long, Long>, DataModel<Long, Long>, double, int[]) - Constructor for class net.recommenders.rival.evaluation.metric.ranking.Recall
Constructor where the cutoff levels can be initialized
recommend(long, int, IDRescorer) - Method in class net.recommenders.rival.recommend.frameworks.mahout.PopularityBasedRecommender
Recommend items to a user.
recommend(Properties) - Static method in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
Run recommendations based on properties
RECOMMENDATION_FOLDER - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
RECOMMENDATION_FOLDER - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
RECOMMENDATION_SUFFIX - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
RECOMMENDATION_SUFFIX - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
RecommendationRunner - Class in net.recommenders.rival.recommend.frameworks
 
RecommendationRunner() - Constructor for class net.recommenders.rival.recommend.frameworks.RecommendationRunner
 
recommender - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the recommender
RecommenderException - Exception in net.recommenders.rival.recommend.frameworks.mahout.exceptions
 
RecommenderException(String) - Constructor for exception net.recommenders.rival.recommend.frameworks.mahout.exceptions.RecommenderException
 
RecommenderException(String, Throwable) - Constructor for exception net.recommenders.rival.recommend.frameworks.mahout.exceptions.RecommenderException
 
refresh(Collection<Refreshable>) - Method in class net.recommenders.rival.recommend.frameworks.mahout.PopularityBasedRecommender
 
RELEVANCE_THRESHOLD - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
RELEVANCE_THRESHOLD - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
RELEVANCE_THRESHOLD - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
RELEVANCE_THRESHOLD - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
RELEVANCE_THRESHOLDS - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
RELEVANCE_THRESHOLDS - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
RelPlusN - Class in net.recommenders.rival.evaluation.strategy
Implementation of the Relevant + N Evaluation Strategy as described by Cremonesi et al.
RelPlusN(DataModel<Long, Long>, DataModel<Long, Long>, int, double, long) - Constructor for class net.recommenders.rival.evaluation.strategy.RelPlusN
Default constructor for the strategy.
RELPLUSN_N - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
RELPLUSN_N - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
RELPLUSN_N - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
RELPLUSN_N - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
RELPLUSN_SEED - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
RELPLUSN_SEED - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
RELPLUSN_SEED - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
RELPLUSN_SEED - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
RMSE - Class in net.recommenders.rival.evaluation.metric.error
Root mean square error (RMSE) of a list of predicted ratings.
RMSE(DataModel<Long, Long>, DataModel<Long, Long>) - Constructor for class net.recommenders.rival.evaluation.metric.error.RMSE
Default constructor with predictions and groundtruth information
RMSE(DataModel<Long, Long>, DataModel<Long, Long>, AbstractErrorMetric.ErrorStrategy) - Constructor for class net.recommenders.rival.evaluation.metric.error.RMSE
Constructor where the error strategy can be initialized
run(Properties) - Static method in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
Run a single evaluation metric.
run(Properties) - Static method in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
Run multiple evaluation metrics.
run(Properties) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
Method that runs several strategies (depending on the properties).
run(Properties) - Static method in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
Method that runs several strategies (depending on the properties) where the information is not completely stored in memory.
run(Properties) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunner
Run a single evaluation strategy.
run(Properties) - Static method in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
Process the property file and runs the specified strategies on some data.
run() - Method in class net.recommenders.rival.recommend.frameworks.AbstractRunner
 
run() - Method in class net.recommenders.rival.recommend.frameworks.lenskit.LenskitRecommenderRunner
 
run() - Method in class net.recommenders.rival.recommend.frameworks.mahout.MahoutRecommenderRunner
 
run(Properties) - Static method in class net.recommenders.rival.split.parser.ParserRunner
Run the parser based on given properties.
run(Properties, DataModel<Long, Long>, boolean) - Static method in class net.recommenders.rival.split.splitter.SplitterRunner
Runs a Splitter instance based on the properties.
runLenskitRecommenders(Set<String>, Properties, String[], String[]) - Static method in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 
runMahoutRecommenders(Set<String>, Properties, String[], String[]) - Static method in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 

S

saveDataModel(DataModel<Long, Long>, String, boolean) - Static method in class net.recommenders.rival.split.splitter.SplitterRunner
Method that saves a data model to a file.
setFileName() - Method in class net.recommenders.rival.recommend.frameworks.AbstractRunner
Create the file name of the output file.
setProperties(Properties) - Method in class net.recommenders.rival.recommend.frameworks.AbstractRunner
Sets the properties.
setProperties(Properties) - Method in class net.recommenders.rival.recommend.frameworks.mahout.MahoutRecommenderRunner
 
similarity - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the similarity
SimpleParser - Class in net.recommenders.rival.core
Data parser for tab-separated data files.
SimpleParser() - Constructor for class net.recommenders.rival.core.SimpleParser
 
split(DataModel<Long, Long>) - Method in class net.recommenders.rival.split.splitter.CrossValidationSplitter
 
split(DataModel<Long, Long>) - Method in class net.recommenders.rival.split.splitter.RandomSplitter
 
Split - Class in net.recommenders.rival.split.splitter
Main class that parses a data set and splits it according to a property file.
Split() - Constructor for class net.recommenders.rival.split.splitter.Split
 
split(DataModel<U, I>) - Method in interface net.recommenders.rival.split.splitter.Splitter
Splits the data.
split(DataModel<Long, Long>) - Method in class net.recommenders.rival.split.splitter.TemporalSplitter
 
SPLIT_CV_NFOLDS - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_OUTPUT_FOLDER - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_OUTPUT_OVERWRITE - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_PERITEMS - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_PERUSER - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_RANDOM_PERCENTAGE - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_SEED - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_TEST_PREFIX - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_TEST_SUFFIX - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_TRAINING_PREFIX - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLIT_TRAINING_SUFFIX - Static variable in class net.recommenders.rival.split.splitter.SplitterRunner
 
SPLITS_FOLDER - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
Variables that represent the name of several properties in the file.
SPLITS_FOLDER - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
Variables that represent the name of several properties in the file.
Splitter<U,I> - Interface in net.recommenders.rival.split.splitter
Interface for the data splitter.
SplitterRunner - Class in net.recommenders.rival.split.splitter
Class that splits a dataset according to some properties.
SplitterRunner() - Constructor for class net.recommenders.rival.split.splitter.SplitterRunner
 
statPath - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The canonical path
STRATEGIES - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
STRATEGIES - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
STRATEGY - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
STRATEGY - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
StrategyRunner - Class in net.recommenders.rival.evaluation.strategy
Runner for a single strategy.
StrategyRunner() - Constructor for class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
StrategyRunnerInfile - Class in net.recommenders.rival.evaluation.strategy
Runner for a strategy where the information is not completely stored in memory, only in a per user basis.
StrategyRunnerInfile() - Constructor for class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
SVD_ITER - Static variable in class net.recommenders.rival.recommend.frameworks.MultipleRecommendationRunner
 

T

TemporalSplitter - Class in net.recommenders.rival.split.splitter
Splitter that takes into account the timestamps in the data (older interactions are kept only in the training set).
TemporalSplitter(float, boolean, boolean) - Constructor for class net.recommenders.rival.split.splitter.TemporalSplitter
Constructor
TEST_FILE - Static variable in class net.recommenders.rival.evaluation.metric.EvaluationMetricRunner
 
TEST_FILE - Static variable in class net.recommenders.rival.evaluation.metric.MultipleEvaluationMetricRunner
 
TEST_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
 
TEST_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
 
TEST_SUFFIX - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
TEST_SUFFIX - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
TestItems - Class in net.recommenders.rival.evaluation.strategy
An evaluation strategy where only the test items are used as candidates.
TestItems(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.strategy.TestItems
 
testSet - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the test set
time - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The execution time
TIME_TOK - Static variable in class net.recommenders.rival.core.SimpleParser
The column index for the time in the file.
TIME_TOK - Static variable in class net.recommenders.rival.split.parser.LastfmCelma1KParser
The column index for the time in the file.
TIME_TOK - Static variable in class net.recommenders.rival.split.parser.MovielensParser
The column index for the time in the file.
TRACK_TOK - Static variable in class net.recommenders.rival.split.parser.LastfmCelma1KParser
The column index for the track id in the file.
TRACK_TOK - Static variable in class net.recommenders.rival.split.parser.LastfmCelma360KParser
The column index for the track id in the file.
TRAINING_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunner
Variables that represent the name of several properties in the file.
TRAINING_FILE - Static variable in class net.recommenders.rival.evaluation.strategy.StrategyRunnerInfile
Variables that represent the name of several properties in the file.
TRAINING_SUFFIX - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunner
 
TRAINING_SUFFIX - Static variable in class net.recommenders.rival.evaluation.strategy.MultipleStrategyRunnerInfile
 
trainingSet - Static variable in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
The property key for the training set
TrainItems - Class in net.recommenders.rival.evaluation.strategy
An evaluation strategy where only the items in training are used as candidates.
TrainItems(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.strategy.TrainItems
 
TrecEvalParser - Class in net.recommenders.rival.evaluation.parser
A parser based on the format of trec_eval output (no timestamp info).
TrecEvalParser() - Constructor for class net.recommenders.rival.evaluation.parser.TrecEvalParser
 

U

USER_TOK - Static variable in class net.recommenders.rival.core.SimpleParser
The column index for the user id in the file.
USER_TOK - Static variable in class net.recommenders.rival.evaluation.parser.TrecEvalParser
The column index for the user id in the file.
USER_TOK - Static variable in class net.recommenders.rival.split.parser.LastfmCelma1KParser
The column index for the user id in the file.
USER_TOK - Static variable in class net.recommenders.rival.split.parser.LastfmCelma360KParser
The column index for the user id in the file.
USER_TOK - Static variable in class net.recommenders.rival.split.parser.MovielensParser
The column index for the user id in the file.
UserTest - Class in net.recommenders.rival.evaluation.strategy
An evaluation strategy where only the items in the user's test are used as candidates.
UserTest(DataModel<Long, Long>, DataModel<Long, Long>, double) - Constructor for class net.recommenders.rival.evaluation.strategy.UserTest
 

V

valueOf(String) - Static method in enum net.recommenders.rival.evaluation.metric.error.AbstractErrorMetric.ErrorStrategy
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum net.recommenders.rival.evaluation.metric.ranking.NDCG.TYPE
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum net.recommenders.rival.evaluation.strategy.EvaluationStrategy.OUTPUT_FORMAT
Returns the enum constant of this type with the specified name.
values() - Static method in enum net.recommenders.rival.evaluation.metric.error.AbstractErrorMetric.ErrorStrategy
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum net.recommenders.rival.evaluation.metric.ranking.NDCG.TYPE
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum net.recommenders.rival.evaluation.strategy.EvaluationStrategy.OUTPUT_FORMAT
Returns an array containing the constants of this enum type, in the order they are declared.

W

writeData(long, List<T>) - Method in class net.recommenders.rival.recommend.frameworks.AbstractRunner
Write recommendations to file.
writeStats(String, String, long) - Static method in class net.recommenders.rival.recommend.frameworks.RecommendationRunner
Write the system stats to file
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