This package, SparseMatrixRecommender, has different functions for computations of recommendations
based (user) profile or history using Sparse matrix Linear Algebra. Initially the package mirrored
the Mathematica implementation [AAp1].
The package is based on a certain "standard" Information retrieval paradigm -- it utilizes Latent Semantic Indexing (LSI) functions like IDF, TF-IDF, etc. Hence the package also has document-term matrix creation functions and LSI application functions. I included them in the package since I wanted to minimize the external package dependencies.
See the notebook SMR-creation-and-usage-example.Rmd or the corresponding HTML notebook for examples.
The package includes two data-sets dfTitanic and dfMushroom in order to make easier the
writing of introductory examples and unit tests.
For more theoretical description see the article [AA1].
The package
SMRMon-R,
[AAp2], implements a software monad for SMR workflows.
Most of SMRMon-R functions delegate to SparseMatrixRecommender.
The package
SparseMatrixRecommenderInterfaces,
[AAp3], provides functions for interactive
Shiny
interfaces for the recommenders made with SparseMatrixRecommender and/or SMRMon-R.
The package
LSAMon-R,
[AAp4], can be used to make matrices for SparseMatrixRecommender.
[AA1] Anton Antonov, Mapping Sparse Matrix Recommender to Streams Blending Recommender (2017), MathematicaForPrediction at GitHub.
[AAp1] Anton Antonov, Sparse matrix recommender framework in Mathematica, (2014), MathematicaForPrediction at GitHub.
[AAp2] Anton Antonov, Sparse Matrix Recommender Monad in R (2019), R-packages at GitHub.
[AAp3] Anton Antonov, Sparse Matrix Recommender framework interface functions (2019), R-packages at GitHub.
[AAp4] Anton Antonov, Latent Semantic Analysis Monad in R (2019), R-packages at GitHub.