1mlpack_cf(1)                     User Commands                    mlpack_cf(1)
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NAME

6       mlpack_cf - collaborative filtering
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SYNOPSIS

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

12       This  program  performs  collaborative  filtering  (CF)  on  the  given
13       dataset. Given a list of user,  item  and  preferences  (the  '--train‐
14       ing_file (-t)' parameter), the program will perform a matrix decomposi‐
15       tion and then can perform a series of actions related to  collaborative
16       filtering. Alternately, the program can load an existing saved CF model
17       with the '--input_model_file (-m)' parameter and then use that model to
18       provide recommendations or predict values.
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20       The input matrix should be a 3-dimensional matrix of ratings, where the
21       first dimension is the user, the second dimension is the item, and  the
22       third  dimension is that user's rating of that item. Both the users and
23       items should be numeric indices, not names. The indices are assumed  to
24       start from 0.
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26       A  set of query users for which recommendations can be generated may be
27       specified with the '--query_file (-q)' parameter;  alternately,  recom‐
28       mendations may be generated for every user in the dataset by specifying
29       the ’--all_user_recommendations (-A)' parameter. In addition, the  num‐
30       ber  of  recommendations per user to generate can be specified with the
31       ’--recommendations (-c)' parameter, and the  number  of  similar  users
32       (the  size of the neighborhood) to be considered when generating recom‐
33       mendations can be specified with the '--neighborhood (-n)' parameter.
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35       For performing the matrix  decomposition,  the  following  optimization
36       algorithms can be specified via the '--algorithm (-a)' parameter:
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38              ·  ’RegSVD' -- Regularized SVD using a SGD optimizer
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40              ·  ’NMF'  --  Non-negative matrix factorization with alternating
41                 least squares update rules
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43              ·  ’BatchSVD' -- SVD batch learning
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45              ·  ’SVDIncompleteIncremental'  --  SVD  incomplete   incremental
46                 learning
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48              ·  ’SVDCompleteIncremental' -- SVD complete incremental learning
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50       A  trained  model  may  be saved to with the '--output_model_file (-M)'
51       output parameter.
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53       To train a CF model on  a  dataset  'training_set.csv'  using  NMF  for
54       decomposition  and  saving  the trained model to 'model.bin', one could
55       call:
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57       $  cf   --training_file   training_set.csv   --algorithm   NMF   --out‐
58       put_model_file model.bin
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60       Then,  to  use  this  model to generate recommendations for the list of
61       users in the query set 'users.csv', storing 5 recommendations in  'rec‐
62       ommendations.csv', one could call
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64       $  cf --input_model_file model.bin --query_file users.csv --recommenda‐
65       tions 5 --output_file recommendations.csv
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OPTIONAL INPUT OPTIONS

68       --algorithm (-a) [string]
69              Algorithm used for matrix factorization.  Default value 'NMF'.
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71       --all_user_recommendations (-A) [bool]
72              Generate recommendations for all users.
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74       --help (-h) [bool]
75              Default help info.
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77       --info [string]
78              Get help on a specific module or option.  Default value ''.
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80       --input_model_file (-m) [unknown]
81              Trained CF model to load. Default value ''.
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83       --iteration_only_termination (-I) [bool]
84              Terminate only when the maximum number of iterations is reached.
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86       --max_iterations (-N) [int]
87              Maximum number of iterations. If set to zero, there is no  limit
88              on the number of iterations.  Default value 1000.
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90       --min_residue (-r) [double]
91              Residue  required  to  terminate the factorization (lower values
92              generally mean better fits).  Default value 1e-05.
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94       --neighborhood (-n) [int]
95              Size of the neighborhood of similar users to consider  for  each
96              query user. Default value 5.
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98       --query_file (-q) [string]
99              List  of  query users for which recommendations should be gener‐
100              ated. Default value ''.
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102       --rank (-R) [int]
103              Rank of decomposed matrices (if 0, a heuristic is used to  esti‐
104              mate  the  rank). Default value 0.  --recommendations (-c) [int]
105              Number of recommendations  to  generate  for  each  query  user.
106              Default value 5.
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108       --seed (-s) [int]
109              Set the random seed (0 uses std::time(NULL)).  Default value 0.
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111       --test_file (-T) [string]
112              Test set to calculate RMSE on. Default value ''.
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114       --training_file (-t) [string]
115              Input dataset to perform CF on. Default value ''.
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117       --verbose (-v) [bool]
118              Display  informational  messages and the full list of parameters
119              and timers at the end of execution.
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121       --version (-V) [bool]
122              Display the version of mlpack.
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OPTIONAL OUTPUT OPTIONS

125       --output_file (-o) [string]
126              Matrix that will store output  recommendations.   Default  value
127              ''.
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129       --output_model_file (-M) [unknown]
130              Output for trained CF model. Default value ''.
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ADDITIONAL INFORMATION

133       For further information, including relevant papers, citations, and the‐
134       ory,  consult  the  documentation  found  at  http://www.mlpack.org  or
135       included with your distribution of mlpack.
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139mlpack-3.0.4                   21 February 2019                   mlpack_cf(1)