xenial (1) mlpack_cf.1.gz

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)