Package: rrecsys 0.9.7.2

Ludovik Çoba

rrecsys: Environment for Evaluating Recommender Systems

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi:10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

Authors:Ludovik Çoba [aut, cre, cph], Markus Zanker [ctb], Panagiotis Symeonidis [ctb]

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rrecsys/json (API)

Peer review:

Bug tracker:https://github.com/ludovikcoba/rrecsys/issues

On CRAN:

29 exports 23 stars 2.36 score 35 dependencies 1 mentions 25 scripts 277 downloads

Last updated 2 years agofrom:8511f2770c. Checks:OK: 4 NOTE: 5. Indexed: yes.

Exports:coercecolAveragescolRatingsdataChartdefineDataevalChartevalModelevalPredevalRecgetAUChistogramncolnrownumRatingspredictrankScorerecommendHPRrecommendMFresultsrowAveragesrowRatingsrrecsysrrecsysRegistrysetStoppingCriteriashowshowDeltaErrorshowStoppingCriteriasparsitysummary

Dependencies:clicolorspaceevaluatefansifarverggplot2gluegtablehighrisobandknitrlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppregistryrlangscalestibbleutf8vctrsviridisLitewithrxfunyaml