dissimilarity: Dissimilarity and Similarity Calculation Between Rating Data

In audachang/recommenderlab.test: Lab for Developing and Testing Recommender Algorithms

Description

Calculate dissimilarities/similarities between ratings by users and for items.

Usage

## S4 method for signature 'binaryRatingMatrix'
dissimilarity(x, y = NULL, method = NULL, args = NULL, which="users")
## S4 method for signature 'realRatingMatrix'
dissimilarity(x, y = NULL, method = NULL, args = NULL, which="users")

similarity(x, y = NULL, method = NULL, args = NULL, ...)
## S4 method for signature 'ratingMatrix'
similarity(x, y = NULL, method = NULL, args = NULL, which="users")

Arguments

x	a ratingMatrix.
y	NULL or a second ratingMatrix to calculate cross-(dis)similarities.
method	(dis)similarity measure to use. Available measures are typically "cosine", "pearson", "jaccard", etc. See dissimilarity for class itemMatrix in arules for details about measures for binaryRatingMatrix and dist in proxy for realRatingMatrix.
args	a list of additional arguments for the methods.
which	a character string indicating if the (dis)similarity should be calculated between "users" (rows) or "items" (columns).
...	further arguments.

Details

Similarities are computed from dissimilarities using s=1/(1+d) or s=1-d depending on the measure. For Pearson we use 1 - positive correlation.

Value

returns an object of class dist, simil or an appropriate object (e.g., a matrix) to represent a cross-(dis)similarity.

See Also

ratingMatrix and dissimilarity in arules.

Examples

data(MSWeb)

## between 5 users
dissimilarity(MSWeb[1:5,], method = "jaccard")
similarity(MSWeb[1:5,], method = "jaccard")

## between first 3 items
dissimilarity(MSWeb[,1:3], method = "jaccard", which = "items")
similarity(MSWeb[,1:3], method = "jaccard", which = "items")

## cross-similarity between first 2 users and users 10-20
similarity(MSWeb[1:2,], MSWeb[10:20,], method="jaccard")

audachang/recommenderlab.test documentation built on May 20, 2019, 1:27 p.m.