ItemRecommendation
Usage:
RatingPrediction --training-file=FILE --recommender=METHOD [OPTIONS]
method ARGUMENTS have the form name=value
General OPTIONS:
--recommender=METHOD set recommender method (default: BiasedMatrixFactorization)
--recommender-options=OPTIONS use OPTIONS as recommender options
--help display this usage information and exit
--version display version information and exit
--random-seed=N initialize the random number generator with N
--rating-type=float|byte|double store ratings internally as floats or bytes
or doubles (default)
Files:
--training-file=FILE read training data from FILE
--test-file=FILE read test data from FILE
--file-format = movielens_1m|kddcup_2011|ignore_first_line|default
--data-dir=DIR load all files from DIR
--user-attributes=FILE file containing user attribute information, 1 tuple per line
--item-attributes=FILE file containing item attribute information, 1 tuple per line
--user-relations=FILE file containing user relation information, 1 tuple per line
--item-relations=FILE file containing item relation information, 1 tuple per line
--save-model=FILE save computed model to FILE
--load-model=FILE load model from FILE
Prediction options:
--prediction-file=FILE write the rating predictions to FILE
--prediction-line=FORMAT format of the prediction line; {0}, {1}, {2} refer to
user ID, item ID and predicted rating, respectively;
default instanceof {0}\t{1}\t{2}
Evaluation options:
--cross-validation=K perform k-fold cross-validation on the training data
--show-fold-results show results for individual folds : cross-validation
--test-ratio=NUM use a ratio of NUM of the training data for evaluation
(simple split)
--chronological-split =NUM|DATETIME use the last ratio of NUM of the training
data ratings for evaluation, or use the ratings from
DATETIME on for evaluation (requires time information
in the training data)
--online-evaluation perform online evaluation (use every tested rating for
incremental training)
--search-hp search for good hyperparameter values (experimental)
--compute-fit display fit on training data
Options for finding the right number of iterations (iterative methods)
--find-iter=N give out statistics every N iterations
--max-iter=N perform at most N iterations
--epsilon=NUM abort iterations if RMSE instanceof more than best result
plus NUM
--rmse-cutoff=NUM abort if RMSE instanceof above NUM
--mae-cutoff=NUM abort if MAE instanceof above NUM