/*!

@file cf.txt
@author Ryan Curtin
@brief Tutorial for how to use the CF class and program.

@page cftutorial Collaborative filtering tutorial

@section intro_cftut Introduction

Collaborative filtering is an increasingly popular approach for recommender
systems.  A typical formulation of the problem is as follows: there are \f$n\f$
users and \f$m\f$ items, and each user has rated some of the items.  We want to
provide each user with a recommendation for an item they have not rated yet,
which they are likely to rate highly.  In another formulation, we may want to
predict a user's rating of an item.  This type of problem has been considered
extensively, especially in the context of the Netflix prize.  The winning
approach for the Netflix prize was a collaborative filtering approach which
utilized matrix decomposition.  More information on their approach can be found
in the following paper:

@code
@article{koren2009matrix,
  title={Matrix factorization techniques for recommender systems},
  author={Koren, Yehuda and Bell, Robert and Volinsky, Chris},
  journal={Computer},
  number={8},
  pages={30--37},
  year={2009},
  publisher={IEEE}
}
@endcode

The key to this approach is that the data is represented as an incomplete matrix
\f$V \in \Re^{n \times m}\f$, where \f$V_{ij}\f$ represents user \f$i\f$'s
rating of item \f$j\f$, if that rating exists.  The task, then, is to complete
the entries of the matrix.

In the matrix factorization framework, the matrix \f$V\f$ is assumed to be
low-rank and decomposed into components as \f$V \approx WH\f$ according to some
heuristic.

In order to solve problems of this form, \b mlpack provides:

 - a \ref cli_cftut "simple command-line interface" to perform collaborative filtering
 - a \ref cf_cftut "simple C++ interface" to perform collaborative filtering
 - an \ref cpp_cftut "extensible C++ interface" for implementing new collaborative filtering techniques

@section toc_cftut Table of Contents

 - \ref intro_cftut
 - \ref toc_cftut
 - \ref cli_cftut
   - \ref cli_input_format
   - \ref ex1_cf_cli
   - \ref ex1a_cf_cli
   - \ref ex1b_cf_cli
   - \ref ex2_cf_cli
   - \ref ex3_cf_cli
   - \ref ex4_cf_cli
   - \ref ex5_cf_cli
 - \ref cf_cftut
   - \ref ex1_cf_cpp
   - \ref ex2_cf_cpp
   - \ref ex3_cf_cpp
   - \ref ex4_cf_cpp
 - \ref cpp_cftut
 - \ref further_doc_cftut

@section cli_cftut The 'mlpack_cf' program

\b mlpack provides a command-line program, \c mlpack_cf, which is used to
perform collaborative filtering on a given dataset.  It can provide
neighborhood-based recommendations for users.  The algorithm used for matrix
factorization is configurable, and the parameters of each algorithm are also
configurable.

The following examples detail usage of the \c mlpack_cf program.  Note that you
can get documentation on all the possible parameters by typing:

@code
$ mlpack_cf --help
@endcode

@subsection cli_input_format Input format for mlpack_cf

The input file for the \c mlpack_cf program is specified with the \c
--training_file or \c -t option.  This file is a coordinate-format sparse
matrix, similar to the Matrix Market (MM) format.  The first coordinate is the
user id; the second coordinate is the item id; and the third coordinate is the
rating.  So, for instance, a dataset with 3 users and 2 items, and ratings
between 1 and 5, might look like the following:

@code
$ cat dataset.csv
0, 1, 4
1, 0, 5
1, 1, 1
2, 0, 2
@endcode

This dataset has four ratings: user 0 has rated item 1 with a rating of 4; user
1 has rated item 0 with a rating of 5; user 1 has rated item 1 with a rating of
1; and user 2 has rated item 0 with a rating of 2.  Note that the user and item
indices start from 0, and the identifiers must be numeric indices, and not
names.

The type does not necessarily need to be a csv; it can be any supported storage
format, assuming that it is a coordinate-format file in the format specified
above.  For more information on mlpack file formats, see the documentation for
mlpack::data::Load().

@subsection ex1_cf_cli mlpack_cf with default parameters

In this example, we have a dataset from MovieLens, and we want to use
\c mlpack_cf with the default parameters, which will provide 5 recommendations
for each user, and we wish to save the results in the file
\c recommendations.csv.  Assuming that our dataset is in the file
\c MovieLens-100k.csv and it is in the correct format, we may use the
\c mlpack_cf executable as below:

@code
$ mlpack_cf -t MovieLens-100k.csv -v -o recommendations.csv
@endcode

The \c -v option provides verbose output, and may be omitted if desired.  Now,
for each user, we have recommendations in \c recommendations.csv:

@code
$ head recommendations.csv
317,422,482,356,495
116,120,180,6,327
312,49,116,99,236
312,116,99,236,285
55,190,317,194,63
171,209,180,175,95
208,0,94,87,57
99,97,0,203,172
257,99,180,287,0
171,203,172,209,88
@endcode

So, for user 0, the top 5 recommended items that user 0 has not rated are items
317, 422, 482, 356, and 495.  For user 5, the recommendations are on the sixth
line: 171, 209, 180, 175, 95.

The \c mlpack_cf program can be built into a larger recommendation framework,
with a preprocessing step that can turn user information and item information
into numeric IDs, and a postprocessing step that can map these numeric IDs back
to the original information.

@subsection ex1a_cf_cli Saving mlpack_cf models

The \c mlpack_cf program is able to save a particular model for later loading.
Saving a model can be done with the \c --output_model_file or \c -M option.  The
example below builds a CF model on the \c MovieLens-100k.csv dataset, and then
saves the model to the file \c cf-model.xml for later usage.

@code
$ mlpack_cf -t MovieLens-100k.csv -M cf-model.xml -v
@endcode

The models can also be saved as \c .bin or \c .txt; the \c .xml format provides
a human-inspectable format (though the models tend to be quite complex and may
be difficult to read).  These models can then be re-used to provide specific
recommendations for certain users, or other tasks.

@subsection ex1b_cf_cli Loading mlpack_cf models

Instead of training a model, the \c mlpack_cf model can also load a model to
provide recommendations, using the \c --input_model_file or \c -m option.  For
instance, the example below will load the model from \c cf-model.xml and then
generate 3 recommendations for each user in the dataset, saving the results to
\c recommendations.csv.

@code
$ mlpack_cf -m cf-model.xml -v -o recommendations.csv
@endcode

@subsection ex2_cf_cli Specifying rank of mlpack_cf decomposition

By default, the matrix factorizations in the \c mlpack_cf program decompose the
data matrix into two matrices \f$W\f$ and \f$H\f$ with rank two.  Often, this
default parameter is not correct, and it makes sense to use a higher-rank
decomposition.  The rank can be specified with the \c --rank or \c -R parameter:

@code
$ mlpack_cf -t MovieLens-100k.csv -R 10 -v
@endcode

In the example above, the data matrix will be decomposed into two matrices of
rank 10.  In general, higher-rank decompositions will take longer, but will give
more accurate predictions.

@subsection ex3_cf_cli mlpack_cf with single-user recommendation

In the previous two examples, the output file \c recommendations.csv contains
one line for each user in the input dataset.  But often, recommendations may
only be desired for a few users.  In that case, we can assemble a file of query
users, with one user per line:

@code
$ cat query.csv
0
17
31
@endcode

Now, if we run the \c mlpack_cf executable with this query file, we will obtain
recommendations for users 0, 17, and 31:

@code
$ mlpack_cf -i MovieLens-100k.csv -R 10 -q query.csv -o recommendations.csv
$ cat recommendations.csv
474,356,317,432,473
510,172,204,483,182
0,120,236,257,126
@endcode

@subsection ex4_cf_cli mlpack_cf with non-default factorizer

The \c --algorithm (or \c -a ) parameter controls the factorizer that is used.
Several options are available:

 - \c 'NMF': non-negative matrix factorization; see mlpack::amf::AMF<>
 - \c 'SVDBatch': SVD batch factorization
 - \c 'SVDIncompleteIncremental': incomplete incremental SVD
 - \c 'SVDCompleteIncremental': complete incremental SVD
 - \c 'RegSVD': regularized SVD; see mlpack::svd::RegularizedSVD

The default factorizer is \c 'NMF'.  The example below uses the 'RegSVD'
factorizer:

@code
$ mlpack_cf -i MovieLens-100k.csv -R 10 -q query.csv -a RegSVD -o recommendations.csv
@endcode

@subsection ex5_cf_cli mlpack_cf with non-default neighborhood size

The \c mlpack_cf program produces recommendations using a neighborhood: similar
users in the query user's neighborhood will be averaged to produce predictions.
The size of this neighborhood is controlled with the \c --neighborhood (or \c -n
) option.  An example using a neighborhood with 10 similar users is below:

@code
$ mlpack_cf -i MovieLens-100k.csv -R 10 -q query.csv -a RegSVD -n 10
@endcode

@section cf_cftut The 'CF' class

The \c CF class in \b mlpack offers a simple, flexible API for performing
collaborative filtering for recommender systems within C++ applications.  In the
constructor, the \c CF class takes a coordinate-list dataset and decomposes the
matrix according to the specified \c FactorizerType template parameter.

Then, the \c GetRecommendations() function may be called to obtain
recommendations for certain users (or all users), and the \c W() and \c H()
matrices may be accessed to perform other computations.

The data which the \c CF constructor takes should be an Armadillo matrix (\c
arma::mat ) with three rows.  The first row corresponds to users; the second
row corresponds to items; the third column corresponds to the rating.  This is a
coordinate list format, like the format the \c cf executable takes.  The
data::Load() function can be used to load data.

The following examples detail a few ways that the \c CF class can be used.

@subsection ex1_cf_cpp CF with default parameters

This example constructs the \c CF object with default parameters and obtains
recommendations for each user, storing the output in the \c recommendations
matrix.

@code
#include <mlpack/methods/cf/cf.hpp>

using namespace mlpack::cf;

// The coordinate list of ratings that we have.
extern arma::mat data;
// The size of the neighborhood to use to get recommendations.
extern size_t neighborhood;
// The rank of the decomposition.
extern size_t rank;

// Build the CF object and perform the decomposition.
// The constructor takes a default-constructed factorizer, which, by default,
// is of type amf::NMFALSFactorizer.
CF cf(data, amf::NMFALSFactorizer(), neighborhood, rank);

// Store the results in this object.
arma::Mat<size_t> recommendations;

// Get 5 recommendations for all users.
cf.GetRecommendations(5, recommendations);
@endcode

@subsection ex2_cf_cpp CF with other factorizers

\b mlpack provides a number of existing factorizers which can be used in place
of the default mlpack::amf::NMFALSFactorizer (which is non-negative matrix
factorization with alternating least squares update rules).  These include:

 - mlpack::amf::SVDBatchFactorizer
 - mlpack::amf::SVDCompleteIncrementalFactorizer
 - mlpack::amf::SVDIncompleteIncrementalFactorizer
 - mlpack::amf::NMFALSFactorizer
 - mlpack::svd::RegularizedSVD
 - mlpack::svd::QUIC_SVD

The amf::AMF<> class has many other possibilities than those listed here; it is
a framework for alternating matrix factorization techniques.  See the
\ref mlpack::amf::AMF<> "class documentation" or \ref amftutorial "tutorial on AMF" for
more information.

The use of another factorizer is straightforward; the example from the previous
section is adapted below to use svd::RegularizedSVD:

@code
#include <mlpack/methods/cf/cf.hpp>
#include <mlpack/methods/regularized_svd/regularized_svd.hpp>

using namespace mlpack::cf;

// The coordinate list of ratings that we have.
extern arma::mat data;
// The size of the neighborhood to use to get recommendations.
extern size_t neighborhood;
// The rank of the decomposition.
extern size_t rank;

// Build the CF object and perform the decomposition.
CF cf(data, svd::RegularizedSVD(), neighborhood, rank);

// Store the results in this object.
arma::Mat<size_t> recommendations;

// Get 5 recommendations for all users.
cf.GetRecommendations(5, recommendations);
@endcode

@subsection ex3_cf_cpp Predicting individual user/item ratings

The \c Predict() method can be used to predict the rating of an item by a
certain user, using the same neighborhood-based approach as the
\c GetRecommendations() function or the \c cf executable.  Below is an example
of the use of that function.

The example below will obtain the predicted rating for item 50 by user 12.

@code
#include <mlpack/methods/cf/cf.hpp>

using namespace mlpack::cf;

// The coordinate list of ratings that we have.
extern arma::mat data;
// The size of the neighborhood to use to get recommendations.
extern size_t neighborhood;
// The rank of the decomposition.
extern size_t rank;

// Build the CF object and perform the decomposition.
// The constructor takes a default-constructed factorizer, which, by default,
// is of type amf::NMFALSFactorizer.
CF cf(data, amf::NMFALSFactorizer(), neighborhood, rank);

const double prediction = cf.Predict(12, 50); // User 12, item 50.
@endcode

@subsection ex4_cf_cpp Other operations with the W and H matrices

Sometimes, the raw decomposed W and H matrices can be useful.  The example below
obtains these matrices, and multiplies them against each other to obtain a
reconstructed data matrix with no missing values.

@code
#include <mlpack/methods/cf/cf.hpp>

using namespace mlpack::cf;

// The coordinate list of ratings that we have.
extern arma::mat data;
// The size of the neighborhood to use to get recommendations.
extern size_t neighborhood;
// The rank of the decomposition.
extern size_t rank;

// Build the CF object and perform the decomposition.
// The constructor takes a default-constructed factorizer, which, by default,
// is of type amf::NMFALSFactorizer.
CF cf(data, amf::NMFALSFactorizer(), neighborhood, rank);

// References to W and H matrices.
const arma::mat& W = cf.W();
const arma::mat& H = cf.H();

// Multiply the matrices together.
arma::mat reconstructed = W * H;
@endcode

@section cpp_cftut Template parameters for the 'CF' class

The \c CF class takes the \c FactorizerType as a template parameter to some of
its constructors and to the \c Train() function.  The \c FactorizerType class
defines the algorithm used for matrix factorization.  There are a number of
existing factorizers that can be used in \b mlpack; these were detailed in the
\ref ex2_cf_cpp "'other factorizers' example" of the previous section.

The \c FactorizerType class must implement one of the two following methods:

 - <tt>Apply(arma::mat& data, const size_t rank, arma::mat& W, arma::mat&
   H);</tt>
 - <tt>Apply(arma::sp_mat& data, const size_t rank, arma::mat& W, arma::mat&
   H);</tt>

The difference between these two methods is whether \c arma::mat or \c
arma::sp_mat is used as input.  If \c arma::mat is used, then the data matrix is
a coordinate list with three columns, as in the constructor to the \c CF class.
If \c arma::sp_mat is used, then a sparse matrix is passed with the number of
rows equal to the number of items and the number of columns equal to the number
of users, and each nonzero element in the matrix corresponds to a non-missing
rating.

The method that the factorizer implements is specified via the \c
FactorizerTraits class, which is a template metaprogramming traits class:

@code
template<typename FactorizerType>
struct FactorizerTraits
{
  /**
   * If true, then the passed data matrix is used for factorizer.Apply().
   * Otherwise, it is modified into a form suitable for factorization.
   */
  static const bool UsesCoordinateList = false;
};
@endcode

If \c FactorizerTraits<MyFactorizer>::UsesCoordinateList is \c true, then \c CF
will try to call \c Apply() with an \c arma::mat object.  Otherwise, \c CF will
try to call \c Apply() with an \c arma::sp_mat object.  Specifying the value of
\c UsesCoordinateList is straightforward; provide this specialization of the
\c FactorizerTraits class:

@code
template<>
struct FactorizerTraits<MyFactorizer>
{
  static const bool UsesCoordinateList = true; // Set your value here.
};
@endcode

The \c Apply() function also takes a reference to the matrices \c W and \c H.
When the \c Apply() function returns, the input data matrix should be decomposed
into these two matrices.  \c W should have number of rows equal to the number of
items and number of columns equal to the \c rank parameter, and \c H should have
number of rows equal to the \c rank parameter, and number of columns equal to
the number of users.

The \ref mlpack::amf::AMF<> "amf::AMF<> class" can be used as a base for
factorizers that alternate between updating \c W and updating \c H.  A useful
reference is the \ref amftutorial "AMF tutorial".

@section further_doc_cftut Further documentation

Further documentation for the \c CF class may be found in the \ref
mlpack::cf "complete API documentation".  In addition, more information on
the \c AMF class of factorizers may be found in its \ref mlpack::amf::AMF<>
"complete API documentation".

*/
