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. Hahsler (2022) .

R package recommenderlab - Lab for Developing and Testing Recommender Algorithms

Maintainer: Michael Hahsler

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

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)

The SVD and LIBMF methods use the optional packages irlba and recosystem, respectively. Install them before using those methods:

install.packages(c("irlba", "recosystem"))

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 = c("https://mhahsler.r-universe.dev",
              "https://cloud.r-project.org/"))

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).

Citation request

To cite package ‘recommenderlab’ in publications use:

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},
}

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.