Creates Linear-FLow model described in Practical Linear Models for Large-Scale One-Class Collaborative Filtering. The goal is to find item-item (or user-user) similarity matrix which is low-rank and has small Frobenius norm. Such double regularization allows to better control the generalization error of the model. Idea of the method is somewhat similar to Sparse Linear Methods(SLIM) but scales to large datasets much better.
LinearFlowR6Class object.
For usage details see Methods, Arguments and Examples sections.
model = LinearFlow$new( rank = 8L,
lambda = 0,
init = NULL,
preprocess = identity,
solve_right_singular_vectors = c("soft_impute", "svd")
...)
model$fit_transform(x, ...)
model$transform(x, ...)
model$predict(x, k, not_recommend = x, ...)
model$components
model$v
model$cross_validate_lambda(x, x_train, x_test, lambda = "auto@10",
metric = "map@10", not_recommend = x_train, ...)
$new(rank = 8L, lambda = 0,
init = NULL,
preprocess = identity,
solve_right_singular_vectors = c("svd", "soft_impute"),
...)creates Linear-FLow model with rank latent factors.
If init (right singular vectors of the user-item interactions matrix)
is provided then model initialized with its values.
$fit_transform(x, ...)fits model to
an input user-item interaction matrix.
Returns user embeddings matrix of the size n_users * rank
$transform(x, ...)transforms user-item interaction matrix into user-embeddings matrix.
$predict(x, k, not_recommend = x, ...)predicts top k
item ids for users x. Users features should be defined the same way as they were defined in
training data - as sparse matrix. Column names (=item ids) should be in the same order as
in the fit_transform().
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).
$cross_validate_lambda(x, x_train, x_test, lambda = "auto@10", metric = "map@10",
not_recommend = x_train, ...)perfroms search of the
best regularization parameter lambda:
Model is trained on x data
Then model makes predictions based on x_train data
And finally these predications are validated using specified metric against x_test data
lambda = "auto@20" is able to find good value of the parameter$componentsitem factors matrix of size rank * n_items. In the paper this matrix is called Y
$vright singular vector of the user-item matrix. Size is n_items * rank. In the paper this matrix is called v
A LinearFlow model.
An input sparse user-item matrix (inherits from sparseMatrix)
integer - number of latent factors
numeric - regularization parameter or sequence of regularization values for cross_validate_lambda method.
sparse matrix or NULL - points which items should be excluided from recommendations for a user.
By default it excludes previously seen/consumed items.
metric to use in evaluation of top-k recommendations.
Currently only map@k and ndcg@k are supported (k can be any integer).
other arguments (not used at the moment)
# NOT RUN {
data('movielens100k')
train = movielens100k[1:900, ]
cv = movielens100k[901:nrow(movielens100k), ]
model = LinearFlow$new(rank = 10, lambda = 0, init = NULL,
solve_right_singular_vectors = "svd")
user_emb = model$fit_transform(train)
preds = model$predict(cv, k = 10)
# }
Run the code above in your browser using DataCamp Workspace