NAME

       mlpack_cf - collaborating filtering

SYNOPSIS

        mlpack_cf [-h] [-v] [-a string] [-A] [-m string] [-I] [-N int] [-r double] [--neighborhood int] [-o string] [-M string] [-q string] [-R int] [-n int] [-s int] [-T string] [-t string] -V

DESCRIPTION

       This  program  performs collaborative filtering (CF) on the given dataset. Given a list of
       user,  item  and  preferences  (--training_file)  the  program  will  perform   a   matrix
       decomposition and then can perform a series of actions related to collaborative filtering.
       Alternately, the program can load an existing saved CF model with  the  --input_model_file
       (-m) option and then use that model to provide recommendations or predict values.

       The  input file should contain a 3-column matrix of ratings, where the first column is the
       user, the second column is the item, and the third column is that user's  rating  of  that
       item.  Both  the  users  and  items  should be numeric indices, not names. The indices are
       assumed to start from 0.

       A set of query users for which recommendations can be generated may be specified with  the
       --query_file  (-q) option; alternately, recommendations may be generated for every user in
       the dataset by specifying the --all_user_recommendations (-A)  option.  In  addition,  the
       number of recommendations per user to generate can be specified with the --recommendations
       (-r) parameter, and the number of similar users (the  size  of  the  neighborhood)  to  be
       considered  when  generating recommendations can be specified with the --neighborhood (-n)
       option.

       For performing the matrix decomposition, the  following  optimization  algorithms  can  be
       specified  via  the  --algorithm  (-a)  parameter: ’RegSVD' -- Regularized SVD using a SGD
       optimizer ’NMF' -- Non-negative matrix factorization with alternating least squares update
       rules  ’BatchSVD'  --  SVD  batch  learning  ’SVDIncompleteIncremental'  -- SVD incomplete
       incremental learning ’SVDCompleteIncremental' -- SVD complete incremental learning

       A trained model may be saved to a file with the --output_model_file (-M) parameter.

OPTIONS

       --algorithm (-a) [string]
              Algorithm    used    for    matrix    factorization.     Default    value    'NMF'.
              --all_user_recommendations (-A) Generate recommendations for all users.

       --help (-h)
              Default help info.

       --info [string]
              Get  help  on  a  specific module or option.  Default value ''.  --input_model_file
              (-m)  [string]  File  to  load  trained  CF   model   from.   Default   value   ''.
              --iteration_only_termination  (-I)  Terminate  only  when  the  maximum  number  of
              iterations is reached.

       --max_iterations (-N) [int]
              Maximum number of iterations. Default value

              1000.

       --min_residue (-r) [double]
              Residue required to terminate the factorization (lower values generally mean better
              fits).  Default value 1e-05.

       --neighborhood [int]
              Size  of the neighborhood of similar users to consider for each query user. Default
              value 5.

       --output_file (-o) [string]
              File to save output recommendations to. Default value ''.  --output_model_file (-M)
              [string] File to save trained CF model to. Default value ’'.

       --query_file (-q) [string]
              List of users for which recommendations are to be generated. Default value ''.

       --rank (-R) [int]
              Rank  of  decomposed  matrices  (if  0,  a heuristic is used to estimate the rank).
              Default value

              0.

                  --recommendations (-n) [int] Number of recommendations  to  generate  for  each
                  query user. Default value 5.

       --seed (-s) [int]
              Set the random seed (0 uses std::time(NULL)).  Default value 0.

       --test_file (-T) [string]
              Test  set  to  calculate  RMSE on. Default value ’'.  --training_file (-t) [string]
              Input dataset to perform CF on. Default value ’'.

       --verbose (-v)
              Display informational messages and the full list of parameters and  timers  at  the
              end of execution.

       --version (-V)
              Display the version of mlpack.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

       For  further  information,  including  relevant papers, citations, and theory, For further
       information, including relevant papers, citations, and theory, consult  the  documentation
       found  at  http://www.mlpack.org  or included with your consult the documentation found at
       http://www.mlpack.org or included with  your  DISTRIBUTION  OF  MLPACK.   DISTRIBUTION  OF
       MLPACK.

                                                                                     mlpack_cf(1)