Implementation of the Regularized SVD policy to act as a wrapper when accessing Regularized SVD from within CFType. More...
Public Member Functions | |
RegSVDPolicy (const size_t maxIterations=10) | |
Use regularized SVD 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 regularized SVD. 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... | |
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... | |
const arma::mat & | H () const |
Get the User Matrix. More... | |
size_t | MaxIterations () const |
Get the number of iterations. More... | |
size_t & | MaxIterations () |
Modify the number of iterations. More... | |
template < typename Archive > | |
void | serialize (Archive &ar, const uint32_t) |
Serialization. More... | |
const arma::mat & | W () const |
Get the Item Matrix. More... | |
Implementation of the Regularized SVD policy to act as a wrapper when accessing Regularized SVD from within CFType.
An example of how to use RegSVDPolicy in CF is shown below:
Definition at line 41 of file regularized_svd_method.hpp.
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Use regularized SVD method to perform collaborative filtering.
maxIterations | Number of iterations for the power method (Default: 2). |
Definition at line 50 of file regularized_svd_method.hpp.
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Apply Collaborative Filtering to the provided data set using the regularized SVD.
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. |
Definition at line 68 of file regularized_svd_method.hpp.
References RegularizedSVD< OptimizerType >::Apply().
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Get the neighborhood and corresponding similarities for a set of users.
NeighborSearchPolicy | The policy to perform neighbor search. |
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. |
Definition at line 116 of file regularized_svd_method.hpp.
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Return predicted rating given user ID and item ID.
user | User ID. |
item | Item ID. |
Definition at line 86 of file regularized_svd_method.hpp.
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Get predicted ratings for a user.
user | User ID. |
rating | Resulting rating vector. |
Definition at line 98 of file regularized_svd_method.hpp.
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Get the User Matrix.
Definition at line 145 of file regularized_svd_method.hpp.
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Get the number of iterations.
Definition at line 148 of file regularized_svd_method.hpp.
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Modify the number of iterations.
Definition at line 150 of file regularized_svd_method.hpp.
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Serialization.
Definition at line 156 of file regularized_svd_method.hpp.
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Get the Item Matrix.
Definition at line 143 of file regularized_svd_method.hpp.