ratingMatrix-class: Class "ratingMatrix": Virtual Class for Rating Data

In mhahsler/recommenderlab: Lab for Developing and Testing Recommender Algorithms

Class "ratingMatrix": Virtual Class for Rating Data

Description

Defines a common class for rating data.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

[

signature(x = "ratingMatrix", i = "ANY", j = "ANY", drop = "ANY"): subset the rating matrix (drop is ignorred).

coerce

signature(from = "ratingMatrix", to = "list")

coerce

signature(from = "ratingMatrix", to = "data.frame"): a data.frame with three columns. Col 1 contains user ids, col 2 contains item ids and col 3 contains ratings.

colCounts

signature(x = "ratingMatrix"): number of ratings per column.

rowCounts

signature(x = "ratingMatrix"): number of ratings per row.

colMeans

signature(x = "ratingMatrix"): column-wise rating means.

rowMeans

signature(x = "ratingMatrix"): row-wise rating means.

dim

signature(x = "ratingMatrix"): dimensions of the rating matrix.

dimnames<-

signature(x = "ratingMatrix", value = "list"): replace dimnames.

dimnames

signature(x = "ratingMatrix"): retrieve dimnames.

getNormalize

signature(x = "ratingMatrix"): returns a list with normalization information for the matrix (NULL if data is not normalized).

getRatings

signature(x = "ratingMatrix"): returns all ratings in x as a numeric vector.

getRatingMatrix

signature(x = "ratingMatrix"): returns the ratings as a sparse matrix. The format is different for binary and real rating matices.

hasRating

signature(x = "ratingMatrix"): returns a sparse logical matrix with TRUE for user-item combinations which have a rating.

image

signature(x = "ratingMatrix"): plot the matrix.

nratings

signature(x = "ratingMatrix"): number of ratings in the matrix.

sample

signature(x = "ratingMatrix"): sample from users (rows).

show

signature(object = "ratingMatrix")

See Also

See implementing classes realRatingMatrix and binaryRatingMatrix. See getList, getData.frame, similarity, dissimilarity and dissimilarity.

mhahsler/recommenderlab documentation built on March 19, 2024, 5:48 p.m.