MyMediaLiteJava: Documentation

ItemRecommendation

Usage:
     ItemRecommendation --training-file=FILE --recommender=METHOD [OPTIONS]
 
     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 the 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
  --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
  --user-groups=FILE           file containing 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 datasets) interpret rating >= NUM as positive 
                               feedback

Choosing the items for evaluation/prediction (mutually exclusive):
  --candidate-items=FILE       use the items in FILE (one per line) as candidate items in 
                               the evaluation
  --overlap-items              use only the items that are both in the training and the 
                               test set as candidate items in the evaluation
  --in-training-items          use only the items in the training set as candidate items in
                               the evaluation
  --in-test-items              use only the items in the test set as candidate items in 
                               the evaluation
  --all-items                  use all known items as candidate items in the evaluation

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 --predict-items-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 
                               (expects --item-attributes=FILE)
  --repeat-evaluation          assume that items can be accessed repeatedly - items can 
                               occur both in the training and the test data for one user
  --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
  --auc-cutoff=NUM             abort if AUC is below NUM
  --prec5-cutoff=NUM           abort if prec@5 is below NUM