Provided by: mlpack-bin_4.5.0-1_amd64 
NAME
mlpack_cf - collaborative filtering
SYNOPSIS
mlpack_cf [-a string] [-A bool] [-m unknown] [-i string] [-I bool] [-N int] [-r double] [-S string] [-n int] [-z string] [-q unknown] [-R int] [-c int] [-s int] [-T unknown] [-t unknown] [-V bool] [-o unknown] [-M unknown] [-h -v]
DESCRIPTION
This program performs collaborative filtering (CF) on the given dataset. Given a list of
user, item and preferences (the '--training_file (-t)' parameter), 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)' parameter and then use that model to provide recommendations
or predict values.
The input matrix should be a 3-dimensional matrix of ratings, where the first dimension is
the user, the second dimension is the item, and the third dimension 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)' parameter; alternately, recommendations may be generated for every
user in the dataset by specifying the ’--all_user_recommendations (-A)' parameter. In
addition, the number of recommendations per user to generate can be specified with the
’--recommendations (-c)' 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)' parameter.
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
• ’BiasSVD' -- Bias SVD using a SGD optimizer
• ’SVDPP' -- SVD++ using a SGD optimizer
• ’RandSVD' -- RandomizedSVD learning
• ’QSVD' -- QuicSVD learning
• ’BKSVD' -- Block Krylov SVD learning
The following neighbor search algorithms can be specified via the
’--neighbor_search (-S)' parameter:
• ’cosine' -- Cosine Search Algorithm
• ’euclidean' -- Euclidean Search Algorithm
• ’pearson' -- Pearson Search Algorithm
The following weight interpolation algorithms can be specified via the
’--interpolation (-i)' parameter:
• ’average' -- Average Interpolation Algorithm
• ’regression' -- Regression Interpolation Algorithm
• ’similarity' -- Similarity Interpolation Algorithm
The following ranking normalization algorithms can be specified via the
’--normalization (-z)' parameter:
• ’none' -- No Normalization
• ’item_mean' -- Item Mean Normalization
• ’overall_mean' -- Overall Mean Normalization
• ’user_mean' -- User Mean Normalization
• ’z_score' -- Z-Score Normalization
A trained model may be saved to with the '--output_model_file (-M)' output
parameter.
To train a CF model on a dataset 'training_set.csv' using NMF for decomposition and
saving the trained model to 'model.bin', one could call:
$ mlpack_cf --training_file training_set.csv --algorithm NMF --output_model_file
model.bin
Then, to use this model to generate recommendations for the list of users in the
query set 'users.csv', storing 5 recommendations in 'recommendations.csv', one
could call
$ mlpack_cf --input_model_file model.bin --query_file users.csv --recommendations 5
--output_file recommendations.csv
OPTIONAL INPUT OPTIONS
--algorithm (-a) [string]
Algorithm used for matrix factorization. Default value 'NMF'.
--all_user_recommendations (-A) [bool]
Generate recommendations for all users.
--help (-h) [bool]
Default help info.
--info [string]
Print help on a specific option. Default value ''.
--input_model_file (-m) [unknown]
Trained CF model to load.
--interpolation (-i) [string]
Algorithm used for weight interpolation. Default value 'average'.
--iteration_only_termination (-I) [bool]
Terminate only when the maximum number of iterations is reached.
--max_iterations (-N) [int]
Maximum number of iterations. If set to zero, there is no limit on the 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.
--neighbor_search (-S) [string]
Algorithm used for neighbor search. Default value 'euclidean'.
--neighborhood (-n) [int]
Size of the neighborhood of similar users to consider for each query user. Default
value 5.
--normalization (-z) [string]
Normalization performed on the ratings. Default value 'none'.
--query_file (-q) [unknown]
List of query users for which recommendations should be generated.
--rank (-R) [int]
Rank of decomposed matrices (if 0, a heuristic is used to estimate the rank).
Default value 0. --recommendations (-c) [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) [unknown]
Test set to calculate RMSE on.
--training_file (-t) [unknown]
Input dataset to perform CF on.
--verbose (-v) [bool]
Display informational messages and the full list of parameters and timers at the
end of execution.
--version (-V) [bool]
Display the version of mlpack.
OPTIONAL OUTPUT OPTIONS
--output_file (-o) [unknown] Matrix that will store output recommendations.
--output_model_file (-M) [unknown]
Output for trained CF model.
ADDITIONAL INFORMATION
For further information, including relevant papers, citations, and theory, consult the
documentation found at http://www.mlpack.org or included with your distribution of mlpack.