Description

Container for a sparse dataset that distinguishes between binary and non-binary feedback datasets. Data are stored as tuples (user, item, rating). Extends _ds.

Slots

data:

the dataset, class "matrix".

binary:

class "logical", determines if the item dataset contains binary (i.e. 1/0) or non-binary ratings.

minimum:

class "numeric", defines the minimal value present in the dataset.

maximum:

class "numeric", defines the maximal value present in the dataset.

intScale:

object of class "logical", if TRUE the range of ratings in the dataset contains as well half star values.

userID:

class "numeric", array containing all user IDs.

itemID:

class "numeric", array containing all item IDs.

userPointers:

class "list", pointer to all users position in the dataset.

itemPointers:

class "list", pointer to all items position in the dataset.

Methods

nrow

signature(object = "sparseDataSet"): number of rows of the dataset.

ncol

signature(object = "sparseDataSet"): number of columns of the dataset.

dim

signature(object = "sparseDataSet"): returns the dimensions of the dataset.

rowRatings

signature(object = "sparseDataSet"): returns the number of ratings on each row.

colRatings

signature(object = "sparseDataSet"): returns the number of ratings on each column.

numRatings

signature(object = "sparseDataSet"): returns the total number of ratings.

[

signature(x = "sparseDataSet", i = "ANY", j = "ANY", drop = "ANY")): returns a subset of the dataset.

coerce

signature(from = "sparseDataSet", to = "matrix")

rowAverages

signature(object = "sparseDataSet"): returns the average rating on each row.

colAverages

signature(object = "sparseDataSet"): returns the average rating on each column.