Predict entries in columns of the `X` matrix for new users/rows given their new `X` and/or `U` data at the combinations [row,column] given by the entries in `user` and `item` (e.g. passing `user=c(1,2,3), item=c(1,1,1)` will predict X[1,1], X[2,1], X[3,1]).
Note: this function will not perform any internal re-indexing for the data. If the `X` to which the data was fit was a `data.frame`, the numeration of the items will be under `model$info$item_mapping`.
predict_new(model, ...)# S3 method for CMF
predict_new(
model,
items,
rows = NULL,
X = NULL,
U = NULL,
U_bin = NULL,
weight = NULL,
nthreads = model$info$nthreads,
...
)
# S3 method for CMF_implicit
predict_new(
model,
items,
rows = NULL,
X = NULL,
U = NULL,
nthreads = model$info$nthreads,
...
)
# S3 method for OMF_explicit
predict_new(
model,
items,
rows = NULL,
X = NULL,
U = NULL,
weight = NULL,
exact = FALSE,
nthreads = model$info$nthreads,
...
)
# S3 method for OMF_implicit
predict_new(
model,
items,
rows = NULL,
X = NULL,
U = NULL,
nthreads = model$info$nthreads,
...
)
# S3 method for ContentBased
predict_new(
model,
items = NULL,
rows = NULL,
U = NULL,
I = NULL,
nthreads = model$info$nthreads,
...
)
A numeric vector with the predicted values for the requested combinations of users (rows in the new data) and items (columns in the old data, unless passing `I` in which case will be rows of `I`). Invalid combinations will be filled with NAs.
A collective matrix factorization model from this package - see fit_models for details.
Not used.
The item IDs for which to make predictions. If `X` to which the model was fit was a `data.frame`, should pass IDs matching to the second column of `X` (the item indices, should be a character vector), otherwise should pass column numbers for `X`, with numeration starting at 1 (should be an integer vector).
If passing `I`, will instead take these indices as row numbers for `I` (only available for the ContentBased model).
Rows of the new `X`/`U` passed here for which to make predictions, with numeration starting at 1 (should be an integer vector). If not passed and there is only 1 row of data, will predict the entries in `items` for that same row. If not passed and there is more than 1 row of data, the number of rows in the data should match with the number of entries in `items`, and will make predictions for each such combination of <entry in item, row in the data>.
New `X` data, with rows denoting new users. Can be passed in the following formats:
A sparse COO/triplets matrix, either from package `Matrix` (class `dgTMatrix`), or from package `SparseM` (class `matrix.coo`).
A sparse matrix in CSR format, either from package `Matrix` (class `dgRMatrix`), or from package `SparseM` (class `matrix.csr`). Passing the input as CSR is faster than COO as it will be converted internally.
A sparse row vector from package `Matrix` (class `dsparseVector`).
A dense matrix from base R (class `matrix`), with missing entries set as `NA`/`NaN`.
A dense row vector from base R (class `numeric`), with missing entries set as `NA`/`NaN`.
Dense `X` data is not supported for `CMF_implicit` or `OMF_implicit`.
New `U` data, with rows denoting new users. Can be passed in the same formats as `X`, or additionally as a `data.frame`, which will be internally converted to a matrix.
New binary columns of `U`. Must be passed as a dense matrix from base R or as a `data.frame`.
Associated observation weights for entries in `X`. If passed, must have the same shape as `X` - that is, if `X` is a sparse matrix, should be a numeric vector with length equal to the non-missing elements, if `X` is a dense matrix, should also be a dense matrix with the same number of rows and columns.
Number of parallel threads to use.
(In the `OMF_explicit` model) Whether to calculate `A` and `Am` with the regularization applied to `A` instead of to `Am` (if using the L-BFGS method, this is how the model was fit). This is usually a slower procedure.
(For the `ContentBased` model only) New `I` data for which to make predictions. Supports the same formats as `U`.
predict.cmfrec