Implementation of the Bias SVD policy to act as a wrapper when accessing Bias SVD from within CFType.
More...
#include <bias_svd_method.hpp>
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| BiasSVDPolicy (const size_t maxIterations=10, const double alpha=0.02, const double lambda=0.05) |
| Use Bias SVD method to perform collaborative filtering. More...
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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 bias SVD. More...
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double | GetRating (const size_t user, const size_t item) const |
| Return predicted rating given user ID and item ID. More...
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void | GetRatingOfUser (const size_t user, arma::vec &rating) const |
| Get predicted ratings for a user. More...
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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...
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const arma::mat & | W () const |
| Get the Item Matrix.
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const arma::mat & | H () const |
| Get the User Matrix.
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const arma::vec & | Q () const |
| Get the User Bias Vector.
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const arma::vec & | P () const |
| Get the Item Bias Vector.
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size_t | MaxIterations () const |
| Get the number of iterations.
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size_t & | MaxIterations () |
| Modify the number of iterations.
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double | Alpha () const |
| Get learning rate.
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double & | Alpha () |
| Modify learning rate.
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double | Lambda () const |
| Get regularization parameter.
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double & | Lambda () |
| Modify regularization parameter.
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template<typename Archive > |
void | serialize (Archive &ar, const uint32_t) |
| Serialization.
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Implementation of the Bias SVD policy to act as a wrapper when accessing Bias SVD from within CFType.
An example of how to use BiasSVDPolicy in CF is shown below:
extern arma::mat data;
extern arma::Col<size_t> users;
arma::Mat<size_t> recommendations;
CFType<BiasSVDPolicy> cf(data);
cf.GetRecommendations(10, recommendations);
◆ BiasSVDPolicy()
mlpack::cf::BiasSVDPolicy::BiasSVDPolicy |
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const size_t |
maxIterations = 10 , |
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const double |
alpha = 0.02 , |
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const double |
lambda = 0.05 |
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Use Bias SVD method to perform collaborative filtering.
- Parameters
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maxIterations | Number of iterations. |
alpha | Learning rate for optimization. |
lambda | Regularization parameter for optimization. |
◆ Apply()
void mlpack::cf::BiasSVDPolicy::Apply |
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const arma::mat & |
data, |
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const arma::sp_mat & |
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const size_t |
rank, |
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const size_t |
maxIterations, |
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const double |
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const bool |
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inline |
Apply Collaborative Filtering to the provided data set using the bias SVD.
- Parameters
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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::BiasSVDPolicy::GetNeighborhood |
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const arma::Col< size_t > & |
users, |
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const size_t |
numUsersForSimilarity, |
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arma::Mat< size_t > & |
neighborhood, |
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arma::mat & |
similarities |
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Get the neighborhood and corresponding similarities for a set of users.
- Template Parameters
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NeighborSearchPolicy | The policy to perform neighbor search. |
- Parameters
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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::BiasSVDPolicy::GetRating |
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const size_t |
user, |
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const size_t |
item |
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Return predicted rating given user ID and item ID.
- Parameters
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user | User ID. |
item | Item ID. |
◆ GetRatingOfUser()
void mlpack::cf::BiasSVDPolicy::GetRatingOfUser |
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const size_t |
user, |
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arma::vec & |
rating |
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Get predicted ratings for a user.
- Parameters
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user | User ID. |
rating | Resulting rating vector. |
The documentation for this class was generated from the following file: