This class implements Collaborative Filtering (CF). More...

Public Member Functions
	CFType (const size_t numUsersForSimilarity=5, const size_t rank=0)
	Initialize the CFType object without performing any factorization. More...

template < typename MatType , typename DecompositionPolicy = NMFPolicy >
	CFType (const MatType &data, DecompositionPolicy &decomposition=DecompositionPolicy(), const size_t numUsersForSimilarity=5, const size_t rank=0, const size_t maxIterations=1000, const double minResidue=1e-5, const bool mit=false)
	Initialize the CFType object using any decomposition method, immediately factorizing the given data to create a model. More...

const arma::sp_mat &	CleanedData () const
	Get the cleaned data matrix. More...

void	GetRecommendations (const size_t numRecs, arma::Mat< size_t > &recommendations)
	Generates the given number of recommendations for all users. More...

void	GetRecommendations (const size_t numRecs, arma::Mat< size_t > &recommendations, const arma::Col< size_t > &users)
	Generates the given number of recommendations for the specified users. More...

const arma::mat &	H () const
	Get the Item Matrix. More...

void	NumUsersForSimilarity (const size_t num)
	Sets number of users for calculating similarity. More...

size_t	NumUsersForSimilarity () const
	Gets number of users for calculating similarity. More...

double	Predict (const size_t user, const size_t item) const
	Predict the rating of an item by a particular user. More...

void	Predict (const arma::Mat< size_t > &combinations, arma::vec &predictions) const
	Predict ratings for each user-item combination in the given coordinate list matrix. More...

void	Rank (const size_t rankValue)
	Sets rank parameter for matrix factorization. More...

size_t	Rank () const
	Gets rank parameter for matrix factorization. More...

template < typename Archive >
void	serialize (Archive &ar, const unsigned int)
	Serialize the CFType model to the given archive. More...

template < typename DecompositionPolicy >
void	Train (const arma::mat &data, DecompositionPolicy &decomposition, const size_t maxIterations=1000, const double minResidue=1e-5, const bool mit=false)
	Train the CFType model (i.e. More...

template < typename DecompositionPolicy >
void	Train (const arma::sp_mat &data, DecompositionPolicy &decomposition, const size_t maxIterations=1000, const double minResidue=1e-5, const bool mit=false)
	Train the CFType model (i.e. More...

const arma::mat &	W () const
	Get the User Matrix. More...

Static Public Member Functions
static void	CleanData (const arma::mat &data, arma::sp_mat &cleanedData)
	Converts the User, Item, Value Matrix to User-Item Table. More...

Detailed Description

This class implements Collaborative Filtering (CF).

This implementation presently supports Alternating Least Squares (ALS) for collaborative filtering.

A simple example of how to run Collaborative Filtering is shown below.

extern arma::mat data; // (user, item, rating) table
extern arma::Col<size_t> users; // users seeking recommendations
arma::Mat<size_t> recommendations; // Recommendations
CFType cf(data); // Default options.
// Generate 10 recommendations for all users.
cf.GetRecommendations(10, recommendations);
// Generate 10 recommendations for specified users.
cf.GetRecommendations(10, recommendations, users);

The data matrix is a (user, item, rating) table. Each column in the matrix should have three rows. The first represents the user; the second represents the item; and the third represents the rating. The user and item, while they are in a matrix that holds doubles, should hold integer (or size_t) values. The user and item indices are assumed to start at 0.

Template Parameters

DecompositionPolicy The algorithm to use to decompose the rating matrix (a W and H matrix).

Definition at line 62 of file cf.hpp.

Constructor & Destructor Documentation

◆ CFType() [1/2]

CFType	(	const size_t	numUsersForSimilarity = `5`,
		const size_t	rank = `0`
	)

Initialize the CFType object without performing any factorization.

Be sure to call Train() before calling GetRecommendations() or any other functions!

◆ CFType() [2/2]

CFType	(	const MatType &	data,
		DecompositionPolicy &	decomposition = `DecompositionPolicy()`,
		const size_t	numUsersForSimilarity = `5`,
		const size_t	rank = `0`,
		const size_t	maxIterations = `1000`,
		const double	minResidue = `1e-5`,
		const bool	mit = `false`
	)

Initialize the CFType object using any decomposition method, immediately factorizing the given data to create a model.

There are parameters that can be set; default values are provided for each of them. If the rank is left unset (or is set to 0), a simple density-based heuristic will be used to choose a rank.

The provided dataset can be a coordinate list; that is, a 3-row matrix where each column corresponds to a (user, item, rating) entry in the matrix or a sparse matrix representing (user, item) table.

Parameters

data	Data matrix: dense matrix (coordinate lists) or sparse matrix(cleaned).
decomposition	Instantiated DecompositionPolicy object.
numUsersForSimilarity	Size of the neighborhood.
rank	Rank parameter for matrix factorization.
maxIterations	Maximum number of iterations.
minResidue	Residue required to terminate.
mit	Whether to terminate only when maxIterations is reached.

Member Function Documentation

◆ CleanData()

static void CleanData	(	const arma::mat &	data,
		arma::sp_mat &	cleanedData
	)

static

Converts the User, Item, Value Matrix to User-Item Table.

Referenced by CFType::CleanedData().

◆ CleanedData()

const arma::sp_mat& CleanedData ( ) const

inline

Get the cleaned data matrix.

Definition at line 172 of file cf.hpp.

References CFType::CleanData(), CFType::GetRecommendations(), CFType::Predict(), and CFType::serialize().

◆ GetRecommendations() [1/2]

void GetRecommendations	(	const size_t	numRecs,
		arma::Mat< size_t > &	recommendations
	)

Generates the given number of recommendations for all users.

Parameters

numRecs	Number of Recommendations.
recommendations	Matrix to save recommendations into.

Referenced by CFType::CleanedData().

◆ GetRecommendations() [2/2]

void GetRecommendations	(	const size_t	numRecs,
		arma::Mat< size_t > &	recommendations,
		const arma::Col< size_t > &	users
	)

Generates the given number of recommendations for the specified users.

Parameters

numRecs	Number of Recommendations.
recommendations	Matrix to save recommendations.
users	Users for which recommendations are to be generated.

◆ H()

const arma::mat& H ( ) const

inline

Get the Item Matrix.

Definition at line 170 of file cf.hpp.

◆ NumUsersForSimilarity() [1/2]

void NumUsersForSimilarity ( const size_t num )

inline

Sets number of users for calculating similarity.

Definition at line 138 of file cf.hpp.

References Log::Warn.

◆ NumUsersForSimilarity() [2/2]

size_t NumUsersForSimilarity ( ) const

inline

Gets number of users for calculating similarity.

Definition at line 150 of file cf.hpp.

◆ Predict() [1/2]

double Predict	(	const size_t	user,
		const size_t	item
	)		const

Predict the rating of an item by a particular user.

Parameters

user	User to predict for.
item	Item to predict for.

Referenced by CFType::CleanedData().

◆ Predict() [2/2]

void Predict	(	const arma::Mat< size_t > &	combinations,
		arma::vec &	predictions
	)		const

Predict ratings for each user-item combination in the given coordinate list matrix.

The matrix 'combinations' should have two rows and number of columns equal to the number of desired predictions. The first element of each column corresponds to the user index, and the second element of each column corresponds to the item index. The output vector 'predictions' will have length equal to combinations.n_cols, and predictions[i] will be equal to the prediction for the user/item combination in combinations.col(i).

Parameters

combinations	User/item combinations to predict.
predictions	Predicted ratings for each user/item combination.

◆ Rank() [1/2]

void Rank ( const size_t rankValue )

inline

Sets rank parameter for matrix factorization.

Definition at line 156 of file cf.hpp.

◆ Rank() [2/2]

size_t Rank ( ) const

inline

Gets rank parameter for matrix factorization.

Definition at line 162 of file cf.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the CFType model to the given archive.

Referenced by CFType::CleanedData().

◆ Train() [1/2]

void Train	(	const arma::mat &	data,
		DecompositionPolicy &	decomposition,
		const size_t	maxIterations = `1000`,
		const double	minResidue = `1e-5`,
		const bool	mit = `false`
	)

Train the CFType model (i.e.

factorize the input matrix) using the parameters that have already been set for the model (specifically, the rank parameter), and optionally, using the given DecompositionPolicy.

Parameters

data	Input dataset; dense matrix (coordinate lists).
decomposition	Instantiated DecompositionPolicy object.
maxIterations	Maximum number of iterations.
minResidue	Residue required to terminate.
mit	Whether to terminate only when maxIterations is reached.

◆ Train() [2/2]

void Train	(	const arma::sp_mat &	data,
		DecompositionPolicy &	decomposition,
		const size_t	maxIterations = `1000`,
		const double	minResidue = `1e-5`,
		const bool	mit = `false`
	)

Train the CFType model (i.e.

factorize the input matrix) using the parameters that have already been set for the model (specifically, the rank parameter), and optionally, using the given DecompositionPolicy.

Parameters

data	Input dataset; sparse matrix (user item table).
decomposition	Instantiated DecompositionPolicy object.
maxIterations	Maximum number of iterations.
minResidue	Residue required to terminate.
mit	Whether to terminate only when maxIterations is reached.

◆ W()

const arma::mat& W ( ) const

inline

Get the User Matrix.

Definition at line 168 of file cf.hpp.

The documentation for this class was generated from the following file:

src/mlpack/methods/cf/cf.hpp

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ CFType() [1/2]

◆ CFType() [2/2]

Member Function Documentation

◆ CleanData()

◆ CleanedData()

◆ GetRecommendations() [1/2]

◆ GetRecommendations() [2/2]

◆ H()

◆ NumUsersForSimilarity() [1/2]

◆ NumUsersForSimilarity() [2/2]

◆ Predict() [1/2]

◆ Predict() [2/2]

◆ Rank() [1/2]

◆ Rank() [2/2]

◆ serialize()

◆ Train() [1/2]

◆ Train() [2/2]

◆ W()