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R package recommenderlab - Lab for Developing and Testing Recommender Algorithms

Introduction

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.

The following R packages use recommenderlab: cmfrec, crassmat, recometrics, recommenderlabBX, recommenderlabJester, RMOA

Please cite the use of this package as:

Hahsler M (2022). “recommenderlab: An R Framework for Developing and Testing Recommendation Algorithms.” arXiv:2205.12371 [cs.IR]. doi:10.48550/ARXIV.2205.12371 https://doi.org/10.48550/ARXIV.2205.12371.

@Misc{,
  title = {recommenderlab: An R Framework for Developing and Testing Recommendation Algorithms},
  author = {Michael Hahsler},
  year = {2022},
  doi = {10.48550/ARXIV.2205.12371},
  howpublished = {arXiv:2205.12371 [cs.IR]},
  month = {May},
}

Supported algorithms

Recommender algorithm

User-based collaborative filtering (UBCF)
Item-based collaborative 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)

Recommender Evaluation

The framework supports given-n and all-but-x protocols with

Train/test split
Cross-validation
Repeated bootstrap sampling

Available 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.

install.packages("recommenderlab", repos = "https://mhahsler.r-universe.dev")

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.

set.seed(1234)

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[1:300]
rec <- Recommender(train, method = "UBCF")
rec

## Recommender of type 'UBCF' for 'realRatingMatrix' 
## learned using 300 users.

Create top-N recommendations for new users (users 301 and 302).

pre <- predict(rec, MovieLense100[301:302], n = 5)
pre

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

as(pre, "list")

## $`0`
## [1] "Amistad (1997)"                    "Kama Sutra: A Tale of Love (1996)"
## [3] "Farewell My Concubine (1993)"      "Roommates (1995)"                 
## [5] "Fresh (1994)"                     
## 
## $`1`
## [1] "Bitter Moon (1992)"         "Touch of Evil (1958)"      
## [3] "Braindead (1992)"           "Great Dictator, The (1940)"
## [5] "M (1931)"

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)), `item-based CF` = list(name = "IBCF",
    param = list(k = 100)))

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.