README.md

In recommenderlab: Lab for Developing and Testing Recommender Algorithms

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

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

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

• Michael Hahsler (2016). recommenderlab: A Framework for Developing and Testing Recommendation Algorithms, R package. https://CRAN.R-project.org/package=recommenderlab
• recommenderlab reference manual
• Suresh K. Gorakala and Michele Usuelli (2015) Building a Recommendation System with R features the package recommenderlab.

recommenderlab documentation built on Feb. 27, 2021, 1:06 a.m.