mlpack::cf::SVDPlusPlusPolicy Class Reference

Implementation of the SVDPlusPlus policy to act as a wrapper when accessing SVDPlusPlus from within CFType. More...

#include <svdplusplus_method.hpp>

Public Member Functions
	SVDPlusPlusPolicy (const size_t maxIterations=10, const double alpha=0.001, const double lambda=0.1)
	Use SVDPlusPlus method to perform collaborative filtering. More...
void	Apply (const arma::mat &data, const arma::sp_mat &, const size_t rank, const size_t maxIterations, const double, const bool)
	Apply Collaborative Filtering to the provided data set using the svdplusplus. More...
double	GetRating (const size_t user, const size_t item) const
	Return predicted rating given user ID and item ID. More...
void	GetRatingOfUser (const size_t user, arma::vec &rating) const
	Get predicted ratings for a user. More...
template<typename NeighborSearchPolicy >
void	GetNeighborhood (const arma::Col< size_t > &users, const size_t numUsersForSimilarity, arma::Mat< size_t > &neighborhood, arma::mat &similarities) const
	Get the neighborhood and corresponding similarities for a set of users. More...
const arma::mat &	W () const
	Get the Item Matrix.
const arma::mat &	H () const
	Get the User Matrix.
const arma::vec &	Q () const
	Get the User Bias Vector.
const arma::vec &	P () const
	Get the Item Bias Vector.
const arma::mat &	Y () const
	Get the Item Implicit Matrix.
const arma::sp_mat &	ImplicitData () const
	Get Implicit Feedback Data.
size_t	MaxIterations () const
	Get the number of iterations.
size_t &	MaxIterations ()
	Modify the number of iterations.
double	Alpha () const
	Get learning rate.
double &	Alpha ()
	Modify learning rate.
double	Lambda () const
	Get regularization parameter.
double &	Lambda ()
	Modify regularization parameter.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialization.

Detailed Description

Implementation of the SVDPlusPlus policy to act as a wrapper when accessing SVDPlusPlus from within CFType.

An example of how to use SVDPlusPlusPolicy in CF is shown below:

extern arma::mat data; // data is a (user, item, rating) table.
// Users for whom recommendations are generated.
extern arma::Col<size_t> users;
arma::Mat<size_t> recommendations; // Resulting recommendations.

CFType<SVDPlusPlusPolicy> cf(data);

// Generate 10 recommendations for all users.
cf.GetRecommendations(10, recommendations);

Constructor & Destructor Documentation

SVDPlusPlusPolicy()

mlpack::cf::SVDPlusPlusPolicy::SVDPlusPlusPolicy ( const size_t  maxIterations = 10, const double  alpha = 0.001, const double  lambda = 0.1  )

inline

Use SVDPlusPlus method to perform collaborative filtering.

Parameters

maxIterations	Number of iterations.
alpha	Learning rate for optimization.
lambda	Regularization parameter for optimization.

Member Function Documentation

Apply()

void mlpack::cf::SVDPlusPlusPolicy::Apply ( const arma::mat &  data, const arma::sp_mat &  , const size_t  rank, const size_t  maxIterations, const double  , const bool    )

inline

Apply Collaborative Filtering to the provided data set using the svdplusplus.

Parameters

data	Data matrix: dense matrix (coordinate lists) or sparse matrix(cleaned).
*	(cleanedData) item user table in form of sparse matrix.
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.

GetNeighborhood()

template<typename NeighborSearchPolicy >

void mlpack::cf::SVDPlusPlusPolicy::GetNeighborhood ( const arma::Col< size_t > &  users, const size_t  numUsersForSimilarity, arma::Mat< size_t > &  neighborhood, arma::mat &  similarities  ) const

inline

Get the neighborhood and corresponding similarities for a set of users.

Template Parameters

NeighborSearchPolicy

The policy to perform neighbor search.

Parameters

users	Users whose neighborhood is to be computed.
numUsersForSimilarity	The number of neighbors returned for each user.
neighborhood	Neighbors represented by user IDs.
similarities	Similarity between each user and each of its neighbors.

GetRating()

double mlpack::cf::SVDPlusPlusPolicy::GetRating ( const size_t  user, const size_t  item  ) const

inline

Return predicted rating given user ID and item ID.

Parameters

user	User ID.
item	Item ID.

GetRatingOfUser()

void mlpack::cf::SVDPlusPlusPolicy::GetRatingOfUser ( const size_t  user, arma::vec &  rating  ) const

inline

Get predicted ratings for a user.

Parameters

user	User ID.
rating	Resulting rating vector.

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

• src/mlpack/methods/cf/decomposition_policies/svdplusplus_method.hpp