recommenderlab - Lab for Developing and Testing Recommender Algorithms - R package
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README.md

recommenderlab - Lab for Developing and Testing Recommender Algorithms - R package

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This R package provides an infrastructure to test and develop recommender algorithms. The package supports rating (e.g., 1-5 stars) and unary (0-1) data sets. Supported algoritms are:

  • User-based collborative filtering (UBCF)
  • Item-based collborative filtering (IBCF)
  • SVD with column-mean imputation (SVD)
  • Funk SVD (SVDF)
  • Association rule-based recommender (AR)
  • Popular items (POPULAR)
  • Randomly chosen items for comparison (RANDOM)
  • Re-recommend liked items (RERECOMMEND)
  • Hybrid recommendations (HybridRecommender)

For evaluation, the framework supports given-n and all-but-x protocols with

  • Train/test split
  • Cross-validation
  • Repeated bootstrap sampling

Evaluation measures are:

  • Rating errors: MSE, RMSE, MAE
  • Top-N recommendations: TPR/FPR (ROC), precision and recall

Installation

Stable CRAN version: install from within R with

install.packages("recommenderlab")

Current development version: Download package from AppVeyor or install from GitHub (needs devtools).

library("devtools")
install_github("mhahsler/recommenderlab")

Usage

Load the package and prepare a dataset (included in the package).

library("recommenderlab")
data("MovieLense")
### use only users with more than 100 ratings
MovieLense100 <- MovieLense[rowCounts(MovieLense) >100,]
MovieLense100
358 x 1664 rating matrix of class ‘realRatingMatrix’ with 73610 ratings.

Train a user-based collaborative filtering recommender using a small training set.

train <- MovieLense100[1:50]
rec <- Recommender(train, method = "UBCF")
rec
Recommender of type ‘UBCF’ for ‘realRatingMatrix’ 
learned using 50 users.

Create top-N recommendations for new users (users 101 and 102)

pre <- predict(rec, MovieLense100[101:102], n = 10)
pre
Recommendations as ‘topNList’ with n = 10 for 2 users. 
as(pre, "list")
$`291`
 [1] "Alien (1979)"              "Titanic (1997)"           
 [3] "Contact (1997)"            "Aliens (1986)"            
 [5] "Amadeus (1984)"            "Godfather, The (1972)"    
 [7] "Henry V (1989)"            "Sting, The (1973)"        
 [9] "Dead Poets Society (1989)" "Schindler's List (1993)"  

$`292`
 [1] "Usual Suspects, The (1995)" "Amadeus (1984)"            
 [3] "Raising Arizona (1987)"     "Citizen Kane (1941)"       
 [5] "Titanic (1997)"             "Brazil (1985)"             
 [7] "Stand by Me (1986)"         "M*A*S*H (1970)"            
 [9] "Babe (1995)"                "GoodFellas (1990)"   

A simple Shiny App running recommenderlab can be found at https://mhahsler-apps.shinyapps.io/Jester/ (source code).

References