R package recommenderlab - Lab for Developing and Testing Recommender Algorithms

Provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. The package supports rating (e.g., 1-5 stars) and unary (0-1) data sets. Supported algorithms are:

• User-based collborative filtering (UBCF)
• Item-based collborative filtering (IBCF)
• SVD with column-mean imputation (SVD)
• Funk SVD (SVDF)
• Alternating Least Squares (ALS)
• Matrix factorization with LIBMF (LIBMF)
• 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: Install from r-universe.

Usage

Load the package and prepare a dataset (included in the package). The MovieLense data contains user ratings for movies on a 1 to 5 star scale. We only use here users with more than 100 ratings.

library("recommenderlab")
data("MovieLense")

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[1:100]
rec <- Recommender(train, method = "UBCF")
rec

## Recommender of type 'UBCF' for 'realRatingMatrix' 
## learned using 100 users.

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

pre <- predict(rec, MovieLense100[101:102], n = 5)
pre

## Recommendations as 'topNList' with n = 5 for 2 users.

as(pre, "list")

## $`0`
## [1] "M (1931)"                                 
## [2] "Braindead (1992)"                         
## [3] "Charade (1963)"                           
## [4] "Touch of Evil (1958)"                     
## [5] "Die xue shuang xiong (Killer, The) (1989)"
## 
## $`1`
## [1] "SubUrbia (1997)"        "Apostle, The (1997)"    "Dead Man (1995)"       
## [4] "War Room, The (1993)"   "Beautiful Thing (1996)"

Use a 10-fold cross-validation scheme to compare the top-N lists of several algorithms. Movies with 4 or more stars are considered a good recommendation. We plot true negative vs. true positive rate for top-N lists of different lengths.

scheme <- evaluationScheme(MovieLense100, method = "cross-validation", k = 10, 
  given = -5, goodRating = 4)
scheme

## Evaluation scheme using all-but-5 items
## Method: 'cross-validation' with 10 run(s).
## Good ratings: >=4.000000
## Data set: 358 x 1664 rating matrix of class 'realRatingMatrix' with 73610 ratings.

algorithms <- list(
  "random items" = list(name = "RANDOM", param = NULL),
  "popular items" = list(name = "POPULAR", param = NULL),
  "user-based CF" = list(name = "UBCF", param = list(nn = 3))
)

results <- evaluate(scheme, algorithms, type = "topNList",
  n=c(1, 3, 5, 10), progress = FALSE)

plot(results, annotate = 2, legend = "topleft")

Shiny App

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

References

• Michael Hahsler (2022) recommenderlab: An R framework for developing and testing recommendation algorithms. arXiv:2205.12371 [cs.IR]. DOI: 10.48550/arXiv.2205.12371.
• recommenderlab reference manual
• Suresh K. Gorakala and Michele Usuelli (2015) Building a Recommendation System with R (Packt Publishing) features the package recommenderlab.