recommenderlab (version 0.2-3)

dissimilarity: Dissimilarity and Similarity Calculation Between Rating Data

Description

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

Usage

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

Value

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

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.

See Also

'>ratingMatrix and dissimilarity in arules.

Examples

# NOT RUN {
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")
# }