Defines your dataset, if either it is implicit or explicit.
Arguments
data
the dataset, class "matrix".
sparseMatrix
class "logical". If FALSE implies that the imput is a dense two dimensional matrix. If TRUE implies that the imput is arranges as coordinate list where entries are stored as list of (row, column, value) tuples.
binary
class "logical", defines if the item dataset consists of binary (i.e. NA/1) or non-binary ratings. Default value FALSE.
minimum
class "numeric", defines the minimal value present in the dataset. Default value 0.5.
maximum
class "numeric", defines the maximal value present in the dataset. Default value 5.
intScale
object of class "logical", if TRUE the range of ratings in the dataset contains as well half star values. Default value FALSE.
positiveThreshold
class "numeric", in case binary is TRUE, positiveThreshold defines the threshold value for binarizing the dataset (i.e. any rating value >= positiveThreshold will be transformed to 1 and all other values to NA(corresponding to a not rated item). Default value 0.5.