WRMF {rsparse} | R Documentation |
Weighted Regularized Matrix Factorization for collaborative filtering
Description
Creates a matrix factorization model which is solved through Alternating Least Squares (Weighted ALS for implicit feedback). For implicit feedback see "Collaborative Filtering for Implicit Feedback Datasets" (Hu, Koren, Volinsky). For explicit feedback it corresponds to the classic model for rating matrix decomposition with MSE error. These two algorithms are proven to work well in recommender systems.
Super class
rsparse::MatrixFactorizationRecommender
-> WRMF
Methods
Public methods
Inherited methods
Method new()
creates WRMF model
Usage
WRMF$new( rank = 10L, lambda = 0, dynamic_lambda = TRUE, init = NULL, preprocess = identity, feedback = c("implicit", "explicit"), solver = c("conjugate_gradient", "cholesky", "nnls"), with_user_item_bias = FALSE, with_global_bias = FALSE, cg_steps = 3L, precision = c("double", "float"), ... )
Arguments
rank
size of the latent dimension
lambda
regularization parameter
dynamic_lambda
whether 'lambda' is to be scaled according to the number
init
initialization of item embeddings
preprocess
identity()
by default. User spectified function which will be applied to user-item interaction matrix before running matrix factorization (also applied during inference time before making predictions). For example we may want to normalize each row of user-item matrix to have 1 norm. Or applylog1p()
to discount large counts. This corresponds to the "confidence" function from "Collaborative Filtering for Implicit Feedback Datasets" paper. Note that it will not automatically add +1 to the weights of the positive entries.feedback
character
- feedback type - one ofc("implicit", "explicit")
solver
character
- solver name. One ofc("conjugate_gradient", "cholesky", "nnls")
. Usually approximate"conjugate_gradient"
is significantly faster and solution is on par with"cholesky"
."nnls"
performs non-negative matrix factorization (NNMF) - restricts user and item embeddings to be non-negative.with_user_item_bias
bool
controls if model should calculate user and item biases. At the moment only implemented for"explicit"
feedback.with_global_bias
bool
controls if model should calculate global biases (mean). At the moment only implemented for"explicit"
feedback.cg_steps
integer > 0
- max number of internal steps in conjugate gradient (if "conjugate_gradient" solver used).cg_steps = 3
by default. Controls precision of linear equation solution at the each ALS step. Usually no need to tune this parameterprecision
one of
c("double", "float")
. Should embedding matrices be numeric or float (fromfloat
package). The latter is usually 2x faster and consumes less RAM. BUTfloat
matrices are not "base" objects. Use carefully....
not used at the moment
Method fit_transform()
fits the model
Usage
WRMF$fit_transform( x, n_iter = 10L, convergence_tol = ifelse(private$feedback == "implicit", 0.005, 0.001), ... )
Arguments
x
input matrix (preferably matrix in CSC format -'CsparseMatrix'
n_iter
max number of ALS iterations
convergence_tol
convergence tolerance checked between iterations
...
not used at the moment
Method transform()
create user embeddings for new input
Usage
WRMF$transform(x, ...)
Arguments
x
user-item iteraction matrix (preferrably as 'dgRMatrix')
...
not used at the moment
Method clone()
The objects of this class are cloneable with this method.
Usage
WRMF$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
References
Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback datasets." 2008 Eighth IEEE International Conference on Data Mining. Ieee, 2008.
http://www.benfrederickson.com/fast-implicit-matrix-factorization/
Franc, Vojtech, Vaclav Hlavac, and Mirko Navara. "Sequential coordinate-wise algorithm for the non-negative least squares problem." International Conference on Computer Analysis of Images and Patterns. Springer, Berlin, Heidelberg, 2005.
Zhou, Yunhong, et al. "Large-scale parallel collaborative filtering for the netflix prize." International conference on algorithmic applications in management. Springer, Berlin, Heidelberg, 2008.
Examples
data('movielens100k')
train = movielens100k[1:900, ]
cv = movielens100k[901:nrow(movielens100k), ]
model = WRMF$new(rank = 5, lambda = 0, feedback = 'implicit')
user_emb = model$fit_transform(train, n_iter = 5, convergence_tol = -1)
item_emb = model$components
preds = model$predict(cv, k = 10, not_recommend = cv)