Swift Streams Blending Recommender

Swift implementation of a Streams Blending Recommender (SBR) framework.

Generally speaking, SBR is a "computer scientist" implementation of a recommendation system based on sparse linear algebra. See the article "Mapping Sparse Matrix Recommender to Streams Blending Recommender", [AA1], for detailed theoretical description of the data structures and operations with them.

This implementation is closely following the Raku Object-Oriented Programming (OOP) implementation "ML::StreamsBlendingRecommender", [AAp1].

Related implementations are:

• Software monad "MonadicSparseMatrixRecommender", [AAp2], in Mathematica

• Software monad "SMRMon-R", [AAp3], in R

• Object-Oriented Programming (OOP) implementation "SparseMatrixRecommender", [AAp4], in Python

Instead of "monads" the implementations in this package and [AAp1, AAp4] use OOP classes.

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Usage examples

TBD...

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Implementation

UML diagram

The PlantUML spec and diagram can be obtained with the CLI script swiftplantuml of the package "SwiftPlantUML", [MEp1]:

swiftplantuml classdiagram .

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References

Articles

[AA1] Anton Antonov, "Mapping Sparse Matrix Recommender to Streams Blending Recommender", (2019), GitHub/antononcube.

Packages, repositories

[AAp1] Anton Antonov, ML::StreamsBlendingRecommender Raku package, (2021), GitHub/antononcube.

[AAp2] Anton Antonov, Monadic Sparse Matrix Recommender Mathematica package, (2018), GitHub/antononcube.

[AAp3] Anton Antonov, Sparse Matrix Recommender Monad R package, (2018), R-packages at GitHub/antononcube.

[AAp4] Anton Antonov, SparseMatrixRecommender Python package, (2021), Python-packages at GitHub/antononcube.

[MEp1] Marco Eidinger, SwiftPlantUML, (2021-2022), GitHub/MarcoEidinger.