MyMediaLite Rating Prediction 3.11
usage: rating_prediction --training-file=FILE --recommender=METHOD [OPTIONS]
recommenders (plus options and their defaults):
- BiPolarSlopeOne
- FactorWiseMatrixFactorization num_factors=10 shrinkage=25 sensibility=1E-05 num_iter=10 reg_u=15 reg_i=10
supports --find-iter=N
- GlobalAverage
supports --online-evaluation
- ItemAttributeKNN k=80 correlation=BinaryCosine weighted_binary=False alpha=0; baseline predictor: reg_u=15 reg_i=10 num_iter=10
needs --item-attributes=FILE
supports --online-evaluation
- ItemAverage
supports --online-evaluation
- ItemKNN k=80 correlation=BinaryCosine weighted_binary=False alpha=0; baseline predictor: reg_u=15 reg_i=10 num_iter=10
supports --online-evaluation
- MatrixFactorization num_factors=10 regularization=0.015 learn_rate=0.01 learn_rate_decay=1 num_iter=30
supports --find-iter=N, --online-evaluation
- SlopeOne
- UserAttributeKNN k=80 correlation=BinaryCosine weighted_binary=False alpha=0; baseline predictor: reg_u=15 reg_i=10 num_iter=10
needs --user-attributes=FILE
supports --online-evaluation
- UserAverage
supports --online-evaluation
- UserItemBaseline reg_u=15 reg_i=10 num_iter=10
supports --find-iter=N, --online-evaluation
- UserKNN k=80 correlation=BinaryCosine weighted_binary=False alpha=0; baseline predictor: reg_u=15 reg_i=10 num_iter=10
supports --online-evaluation
- TimeAwareBaseline num_iter=30 bin_size=70 beta=0.4 user_bias_learn_rate=0.003 item_bias_learn_rate=0.002 alpha_learn_rate=1E-05 item_bias_by_time_bin_learn_rate=5E-06 user_bias_by_day_learn_rate=0.0025 user_scaling_learn_rate=0.008 user_scaling_by_day_learn_rate=0.002 reg_u=0.03 reg_i=0.03 reg_alpha=50 reg_item_bias_by_time_bin=0.1 reg_user_bias_by_day=0.005 reg_user_scaling=0.01 reg_user_scaling_by_day=0.005
supports --find-iter=N
- TimeAwareBaselineWithFrequencies num_iter=40 bin_size=70 beta=0.4 user_bias_learn_rate=0.00267 item_bias_learn_rate=0.000488 alpha_learn_rate=3.11E-06 item_bias_by_time_bin_learn_rate=0.000115 user_bias_by_day_learn_rate=0.000257 user_scaling_learn_rate=0.00564 user_scaling_by_day_learn_rate=0.00103 reg_u=0.0255 reg_i=0.0255 reg_alpha=3.95 reg_item_bias_by_time_bin=0.0929 reg_user_bias_by_day=0.00231 reg_user_scaling=0.0476 reg_user_scaling_by_day=0.019 frequency_log_base=6.76 item_bias_at_frequency_learn_rate=0.00236 reg_item_bias_at_frequency=1.1E-08
supports --find-iter=N
- CoClustering num_user_clusters=3 num_item_clusters=3 num_iter=30
supports --find-iter=N
- Random
supports --online-evaluation
- Constant constant_rating=1
supports --online-evaluation
- LatentFeatureLogLinearModel num_factors=10 bias_reg=0.01 reg_u=0.015 reg_i=0.015 frequency_regularization=False learn_rate=0.01 bias_learn_rate=1 num_iter=30 loss=RMSE
supports --find-iter=N
- BiasedMatrixFactorization num_factors=10 bias_reg=0.01 reg_u=0.015 reg_i=0.015 frequency_regularization=False learn_rate=0.01 bias_learn_rate=1 learn_rate_decay=1 num_iter=30 bold_driver=False loss=RMSE max_threads=1 naive_parallelization=False
supports --find-iter=N, --online-evaluation
- SVDPlusPlus num_factors=10 regularization=0.015 bias_reg=0.33 frequency_regularization=False learn_rate=0.001 bias_learn_rate=0.7 learn_rate_decay=1 num_iter=30
supports --find-iter=N, --online-evaluation
- SigmoidSVDPlusPlus num_factors=10 regularization=0.015 bias_reg=0.33 frequency_regularization=False learn_rate=0.001 bias_learn_rate=0.7 learn_rate_decay=1 num_iter=30 loss=RMSE
supports --find-iter=N, --online-evaluation
- SocialMF num_factors=10 reg_u=0.015 reg_i=0.015 bias_reg=0.01 social_regularization=1 learn_rate=0.01 bias_learn_rate=1 num_iter=30 bold_driver=False loss=RMSE
needs --user-relations=FILE
supports --find-iter=N, --online-evaluation
- SigmoidItemAsymmetricFactorModel num_factors=10 regularization=0.015 bias_reg=0.33 frequency_regularization=False learn_rate=0.001 bias_learn_rate=0.7 learn_rate_decay=1 num_iter=1 loss=30
supports --find-iter=N, --online-evaluation
- SigmoidUserAsymmetricFactorModel num_factors=10 regularization=0.015 bias_reg=0.33 frequency_regularization=False learn_rate=0.001 bias_learn_rate=0.7 learn_rate_decay=1 num_iter=30 loss=RMSE
supports --find-iter=N, --online-evaluation
- SigmoidCombinedAsymmetricFactorModel num_factors=10 regularization=0.015 bias_reg=0.33 frequency_regularization=False learn_rate=0.001 bias_learn_rate=0.7 learn_rate_decay=1 num_iter=30 loss=RMSE
supports --find-iter=N, --online-evaluation
- NaiveBayes class_smoothing=1 attribute_smoothing=1
needs --item-attributes=FILE
supports --online-evaluation
- ExternalRatingPredictor prediction_file=FILENAME
- GSVDPlusPlus num_factors=10 regularization=0.015 bias_reg=0.33 frequency_regularization=False learn_rate=0.001 bias_learn_rate=0.7 learn_rate_decay=1 num_iter=30
needs --item-attributes=FILE
supports --find-iter=N, --online-evaluation
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 random number generator with N
--rating-type=float|byte store ratings internally as floats (default) or bytes
--no-id-mapping do not map user and item IDs to internal IDs, keep original IDs
files:
--training-file=FILE read training data from FILE
--test-file=FILE read test data from FILE
--test-no-ratings test data contains no rating column
(needs both --prediction-file=FILE and --test-file=FILE)
--file-format=movielens_1m|kddcup_2011|ignore_first_line|default
--data-dir=DIR load all files from DIR
--user-attributes=FILE file with user attribute information, 1 tuple per line
--item-attributes=FILE file with item attribute information, 1 tuple per line
--user-relations=FILE file with user relation information, 1 tuple per line
--item-relations=FILE file with item relation information, 1 tuple per line
--save-model=FILE save computed model to FILE
--load-model=FILE load model from FILE
--save-user-mapping=FILE save user ID mapping to FILE
--save-item-mapping=FILE save item ID mapping to FILE
--load-user-mapping=FILE load user ID mapping from FILE
--load-item-mapping=FILE load item ID mapping 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; default is {0}\t{1}\t{2};
--prediction-header=LINE print LINE to the first line of the prediction file
evaluation options:
--cross-validation=K perform k-fold cross-validation on the training data
--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 feature)
--compute-fit display fit on training data
--measures=LIST comma- or space-separated list of evaluation measures to display (default is RMSE, MAE, CBD)
use --help-measures to get a list of all available measures
options for finding the right number of iterations (iterative methods)
--find-iter=N give out statistics every N iterations
--num-iter=N start measuring at N iterations
--max-iter=N perform at most N iterations
--epsilon=NUM abort iterations if main evaluation measure is more than best result plus NUM
--cutoff=NUM abort if main evaluation measure is above NUM
