rrecsys: Environment for Assessing Recommender Systems

Provides implementations of several popular recommendation systems. They can process standard recommendation datasets (user/item matrix) as input and generate rating predictions and recommendation lists. Standard algorithm implementations included in this package are: Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology for recommender systems using measures such as MAE, RMSE, Precision, Recall, AUC, NDCG, RankScore and coverage measures. The package is intended for rapid prototyping of recommendation algorithms and education purposes.

Install the latest version of this package by entering the following in R:
install.packages("rrecsys")
AuthorLudovik Çoba [aut, cre, cph], Markus Zanker [ctb]
Date of publication2016-06-27 17:31:21
MaintainerLudovik Çoba <lcoba@unishk.edu.al>
LicenseGPL-3
Version0.9.5.4
https://github.com/ludovikcoba/rrecsys

View on CRAN

Functions

algAverageClass Man page
algAverageClass-class Man page
BPRclass Man page
BPRclass-class Man page
coerce,dataSet,matrix-method Man page
colRatings Man page
colRatings,dataSet-method Man page
dataSet Man page
[,dataSet,ANY,ANY,missing-method Man page
dataSet-class Man page
defineData Man page
defineData,matrix-method Man page
dim,dataSet-method Man page
evalModel Man page
evalModel-class Man page
evalModel,dataSet-method Man page
evalPred Man page
evalPred,evalModel,list-method Man page
evalPred,evalModel-method Man page
evalRec Man page
evalRec,evalModel,list-method Man page
evalRec,evalModel-method Man page
getAUC Man page
getAUC,evalModel Man page
getAUC,evalModel-method Man page
IBclass Man page
IBclass-class Man page
mlLatest100k Man page
ncol,dataSet-method Man page
nDCG Man page
nrow,dataSet-method Man page
numRatings Man page
numRatings,dataSet-method Man page
PPLclass Man page
PPLclass-class Man page
predict Man page
predict,algAverageClass-method Man page
predict,BPRclass-method Man page
predict,IBclass-method Man page
predict,SVDclass-method Man page
predict,wALSclass-method Man page
rankScore Man page
recommend Man page
recResultsClass Man page
[,recResultsClass,ANY,missing,missing-method Man page
recResultsClass-class Man page
rowRatings Man page
rowRatings,dataSet-method Man page
rrecsys Man page
rrecsys,dataSet-method Man page
rrecsysRegistry Man page
setStoppingCriteria Man page
show,algAverageClass-method Man page
show,BPRclass-method Man page
show,dataSet-method Man page
showDeltaError Man page
show,evalModel-method Man page
show,IBclass-method Man page
show,PPLclass-method Man page
show,recResultsClass-method Man page
showStoppingCriteria Man page
show,SVDclass-method Man page
show,wALSclass-method Man page
sparsity Man page
sparsity,dataSet-method Man page
SVDclass Man page
SVDclass-class Man page
wALSclass Man page
wALSclass-class Man page

Files

inst
inst/doc
inst/doc/d1_extend.html
inst/doc/b4_funkSVD.html
inst/doc/d1_extend.Rmd
inst/doc/b4_funkSVD.Rmd
inst/doc/a1_dataset.html
inst/doc/c1_evaluation.Rmd
inst/doc/b1_nonpersonalized.html
inst/doc/a0_intro.html
inst/doc/b2_IBCF.R inst/doc/b3_itera.R
inst/doc/b6_wALS.html
inst/doc/b5_BPR.R
inst/doc/a0_intro.Rmd
inst/doc/b7_predictrecommend.html
inst/doc/b7_predictrecommend.R inst/doc/a2_dispacherregistry.R
inst/doc/b3_itera.Rmd
inst/doc/a0_intro.R inst/doc/d1_extend.R inst/doc/b6_wALS.R
inst/doc/b5_BPR.html
inst/doc/b3_itera.html
inst/doc/b5_BPR.Rmd
inst/doc/b7_predictrecommend.Rmd
inst/doc/c1_evaluation.html
inst/doc/a1_dataset.R inst/doc/b4_funkSVD.R
inst/doc/b1_nonpersonalized.Rmd
inst/doc/a1_dataset.Rmd
inst/doc/a2_dispacherregistry.html
inst/doc/c1_evaluation.R
inst/doc/b2_IBCF.Rmd
inst/doc/b1_nonpersonalized.R
inst/doc/a2_dispacherregistry.Rmd
inst/doc/b6_wALS.Rmd
inst/doc/b2_IBCF.html
NAMESPACE
data
data/datalist
data/mlLatest100k.rda
R
R/getPrecRecall.R R/evalPred.R R/ALG_funkSVD.R R/ALG_wALS.R R/some_handy_functions.R R/ALG_mostpopular.R R/evalModel.R R/getAUC.R R/ALG_IB_kNN.R R/AAA_registry.R R/evalRec.R R/AAA_generics.R R/ALG_weightScheme_for_ALS.R R/prediction.R R/ZZZ.R R/rrecsys.R R/convergence.R R/rankScore.R R/AAA_classes.R R/ALG_average.R R/ALG_BPR.R R/defineData.R R/nDCG.R R/calcBias.R R/recommend.R
vignettes
vignettes/d1_extend.Rmd
vignettes/b4_funkSVD.Rmd
vignettes/c1_evaluation.Rmd
vignettes/a0_intro.Rmd
vignettes/b3_itera.Rmd
vignettes/b5_BPR.Rmd
vignettes/b7_predictrecommend.Rmd
vignettes/b1_nonpersonalized.Rmd
vignettes/a1_dataset.Rmd
vignettes/b2_IBCF.Rmd
vignettes/a2_dispacherregistry.Rmd
vignettes/b6_wALS.Rmd
MD5
build
build/vignette.rds
DESCRIPTION
man
man/algAverageClass.Rd man/setStoppingCriteria.Rd man/getAUC.Rd man/wALSclass.Rd man/nDCG.Rd man/IBclass.Rd man/evalModel.Rd man/predict.Rd man/PPLclass.Rd man/evalModel-class.Rd man/recommend.Rd man/rankScore.Rd man/evalPred.Rd man/evalrec.Rd man/dataSet-class.Rd man/BPRclass.Rd man/mlLatest100k.Rd man/rrecsys.Rd man/recResultsClass.Rd man/SVDclass.Rd man/defineData.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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