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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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