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title='MyMediaLite: Item Recommendation Tool'
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MyMediaLite item recommendation from positive-only feedback 2.99
usage: item_recommendation --training-file=FILE --recommender=METHOD [OPTIONS]
methods (plus arguments and their defaults):
- FilterBPRMF num_factors=10 bias_reg=0 reg_u=0.0025 reg_i=0.0025 reg_j=0.00025 num_iter=30 learn_rate=0.05 uniform_user_sampling=True with_replacement=False bold_driver=False fast_sampling_memory_limit=1024 update_j=True init_mean=0 init_stddev=0.1
needs --item-attributes=FILE
supports --find-iter=N, --online-evaluation
- ItemAttributeSVM c=1 gamma=0.002
needs --item-attributes=FILE
- BPRMF num_factors=10 bias_reg=0 reg_u=0.0025 reg_i=0.0025 reg_j=0.00025 num_iter=30 learn_rate=0.05 uniform_user_sampling=True with_replacement=False bold_driver=False fast_sampling_memory_limit=1024 update_j=True init_mean=0 init_stddev=0.1
supports --find-iter=N, --online-evaluation
- ItemAttributeKNN k=80
needs --item-attributes=FILE
- ItemKNN k=80
- MostPopular
supports --online-evaluation
- Random
- UserAttributeKNN k=80
needs --user-attributes=FILE
- UserKNN k=80
- WRMF num_factors=10 regularization=0.015 c_pos=1 num_iter=15 init_mean=0 init_stdev=0.1
supports --find-iter=N, --online-evaluation
- WeightedItemKNN k=80
- WeightedUserKNN k=80
- Zero
- MultiCoreBPRMF num_factors=10 bias_reg=0 reg_u=0.0025 reg_i=0.0025 reg_j=0.00025 num_iter=30 learn_rate=0.05 uniform_user_sampling=True with_replacement=False, bold_driver=False fast_sampling_memory_limit=1024 update_j=True init_mean=0 init_stddev=0.1 max_threads=100
supports --find-iter=N, --online-evaluation
- SoftMarginRankingMF num_factors=10 bias_reg=0 reg_u=0.0025 reg_i=0.0025 reg_j=0.00025 num_iter=30 learn_rate=0.1 bold_driver=False fast_sampling_memory_limit=1024 init_mean=0 init_stddev=0.1
supports --find-iter=N, --online-evaluation
- WeightedBPRMF num_factors=10 bias_reg=0 reg_u=0.0025 reg_i=0.0025 reg_j=0.00025 num_iter=30 bold_driver=False learn_rate=0.05 init_mean=0 init_stddev=0.1
supports --find-iter=N, --online-evaluation
- BPRLinear reg=0.015 num_iter=10 learn_rate=0.05 fast_sampling_memory_limit=1024 init_mean=0 init_stdev=0.1
needs --item-attributes=FILE
supports --find-iter=N
method ARGUMENTS have the form name=value
general OPTIONS:
--recommender=METHOD use METHOD for recommendations (default: MostPopular)
--group-recommender=METHOD use METHOD to combine the predictions for several users
--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
files:
--training-file=FILE read training data from FILE
--test-file=FILE read test data from FILE
--file-format=ignore_first_line|default
--no-id-mapping do not map user and item IDs to internal IDs, keep the original IDs
--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
--user-groups=FILE file with group-to-user mappings, 1 tuple per line
--save-model=FILE save computed model to FILE
--load-model=FILE load model from FILE
data interpretation:
--user-prediction transpose the user-item matrix and perform user prediction instead of item prediction
--rating-threshold=NUM (for rating data) interpret rating >= NUM as positive feedback
choosing the items for evaluation/prediction (mutually exclusive):
--candidate-items=FILE use items in FILE (one per line) as candidate items
--overlap-items use only items that are both in the training and the test set as candidate items
--in-training-items use only items in the training set as candidate items
--in-test-items use only items in the test set as candidate items
--all-items use all known items as candidate items
The default is to use both the items in the training and the test set as candidate items.
choosing the users for evaluation/prediction
--test-users=FILE predict items for users specified in FILE (one user per line)
prediction options:
--prediction-file=FILE write ranked predictions to FILE, one user per line
--predict-items-number=N predict N items per user (needs --prediction-file)
evaluation options:
--cross-validation=K perform k-fold cross-validation on the training data
--show-fold-results show results for individual folds in cross-validation
--test-ratio=NUM evaluate by splitting of a NUM part of the feedback
--num-test-users=N evaluate on only N randomly picked users (to save time)
--online-evaluation perform online evaluation (use every tested user-item combination for incremental training)
--filtered-evaluation perform evaluation filtered by item attribute (requires --item-attributes=FILE)
--repeat-evaluation items accessed by a user before may be in the recommendations (and are not ignored in the evaluation)
--compute-fit display fit on training data
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
--measure=MEASURE the evaluation measure to use for the abort conditions below (default is AUC)
--epsilon=NUM abort iterations if MEASURE is less than best result plus NUM
--cutoff=NUM abort if MEASURE is below NUM
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