HybridRecommender: Create a Hybrid Recommender

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

Description

Creates and combines recommendations using several recommender algorithms.

Usage

HybridRecommender(..., weights = NULL)

Arguments

...	objects of class 'Recommender'.
weights	weights for the recommenders. The recommenders are equally weighted by default.

Details

The hybrid recommender is initialized with a set of Recommender objects trained on the same training set (at least the training sets need to have the same items in the same order).

For creating recommendations (predict), each recommender algorithm is used to create ratings. The individual ratings are combined using weighted sum. Weights can be specified in weights.

Value

An object of class 'Recommender'.

See Also

Recommender

Examples

data("MovieLense")
MovieLense100 <- MovieLense[rowCounts(MovieLense) >100,]
train <- MovieLense100[1:100]
test <- MovieLense100[101:103]

## mix popular movies with a random recommendations for diversity and
## rerecommend some movies the user liked.
recom <- HybridRecommender(
  Recommender(train, method = "POPULAR"),
  Recommender(train, method = "RANDOM"),
  Recommender(train, method = "RERECOMMEND"),
  weights = c(.6, .1, .3)
  )

recom

getModel(recom)

as(predict(recom, test), "list")

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