Creates matrix factorization model based on Soft-SVD. Soft SVD is very similar to truncated SVD with ability do add regularization based on nuclear norm.
PureSVDR6Class object.
For usage details see Methods, Arguments and Examples sections.
model = PureSVD$new(rank = 10L,
lambda = 0,
n_threads = parallel::detectCores(),
init = NULL,
preprocess = identity,
...)
model$fit_transform(x, n_iter = 5L, ...)
model$predict(x, k, not_recommend = x, ...)
model$components
$new(rank = 10L, lambda = 0,
n_threads = parallel::detectCores(),
init = NULL,
preprocess = identity,
...
) creates matrix
factorization model model with at most rank latent factors. If init is not null then initializes
with provided SVD solution
$fit_transform(x, n_iter = 5L, ...)fits model to
an input user-item matrix.
Returns factor matrix for users of size n_users * rank
$predict(x, k, not_recommend = x, ...)predict top k
item ids for users x (= column names from the matrix passed to fit_transform() method).
Users features should be defined the same way as they were defined in training data - as sparse matrix
of confidence values (implicit feedback) or ratings (explicit feedback).
Column names (=item ids) should be in the same order as in the fit_transform().
$componentsitem factors matrix of size rank * n_items
numeric default number of threads to use during training and prediction
(if OpenMP is available).
A PureSVD model.
An input sparse user-item matrix(of class dgCMatrix)
integer - maximum number of latent factors
numeric - regularization parameter for nuclear norm
function = identity() by default. User spectified function which will be applied to user-item interaction matrix
before running matrix factorization (also applied in inference time before making predictions). For example we may
want to normalize each row of user-item matrix to have 1 norm. Or apply log1p() to discount large counts.
numeric default number of threads to use during training and prediction
(if OpenMP is available).
sparse matrix or NULL - points which items should be excluided from recommendations for a user.
By default it excludes previously seen/consumed items.
numeric = -Inf defines early stopping strategy. We stop fitting
when one of two following conditions will be satisfied: (a) we have used
all iterations, or (b) relative change of frobenious norm of the two consequent solution is less then
provided convergence_tol
other arguments. Not used at the moment