Create a Recommender Model

Learns a recommender model from given data.

Usage

Recommender(data, ...)
# S4 method for class 'ratingMatrix'
Recommender(data, method, parameter=NULL)

Arguments

data

training data.

method

a character string defining the recommender method to use (see details).

parameter

parameters for the recommender algorithm.

...

further arguments.

Value

An object of class 'Recommender'.

Details

Recommender uses the registry mechanism from package registry to manage methods. This lets users easily specify and add new methods. The registry is called recommenderRegistry. See examples section. Methods SVD and LIBMF require the suggested packages irlba and recosystem, respectively. Install the corresponding package before using either method.

See also

Recommender, ratingMatrix, predict.

Other recommender models: HybridRecommender(), Recommender-class

Examples

data("MSWeb")
MSWeb10 <- sample(MSWeb[rowCounts(MSWeb) >10,], 100)

rec <- Recommender(MSWeb10, method = "POPULAR")
rec
#> Recommender of type ‘POPULAR’ for ‘binaryRatingMatrix’ 
#> learned using 100 users.

getModel(rec)
#> $topN
#> Recommendations as ‘topNList’ with n = 285 for 1 users. 
#> 
#> $ratings
#> 1 x 285 rating matrix of class ‘realRatingMatrix’ with 285 ratings.
#> 

## save and read a recommender model
saveRDS(rec, file = "rec.rds")
rec2 <- readRDS("rec.rds")
rec2
#> Recommender of type ‘POPULAR’ for ‘binaryRatingMatrix’ 
#> learned using 100 users.
unlink("rec.rds")

## look at registry and a few methods
recommenderRegistry$get_entry_names()
#>  [1] "HYBRID_realRatingMatrix"         "HYBRID_binaryRatingMatrix"      
#>  [3] "ALS_realRatingMatrix"            "ALS_implicit_realRatingMatrix"  
#>  [5] "ALS_implicit_binaryRatingMatrix" "AR_binaryRatingMatrix"          
#>  [7] "IBCF_binaryRatingMatrix"         "IBCF_realRatingMatrix"          
#>  [9] "LIBMF_realRatingMatrix"          "POPULAR_binaryRatingMatrix"     
#> [11] "POPULAR_realRatingMatrix"        "RANDOM_realRatingMatrix"        
#> [13] "RANDOM_binaryRatingMatrix"       "RERECOMMEND_realRatingMatrix"   
#> [15] "RERECOMMEND_binaryRatingMatrix"  "SVD_realRatingMatrix"           
#> [17] "SVDF_realRatingMatrix"           "UBCF_binaryRatingMatrix"        
#> [19] "UBCF_realRatingMatrix"          

recommenderRegistry$get_entry("POPULAR", dataType = "binaryRatingMatrix")
#> Recommender method: POPULAR for binaryRatingMatrix Description:
#>   Recommender based on item popularity. Reference: NA
#> Parameters: None

recommenderRegistry$get_entry("SVD", dataType = "realRatingMatrix")
#> Recommender method: SVD for realRatingMatrix Description: Recommender
#>   based on SVD approximation with column-mean imputation. Reference: NA
#> Parameters:
#>    k maxiter normalize
#> 1 10     100  "center"