Java-StreamsBlendingRecommender

Java 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 loosely based on the:

• Software monad "MonadicSparseMatrixRecommender", [AAp1], in Mathematica

• Software monad "SMRMon-R", [AAp2], in R

• Object-Oriented Programming (OOP) implementation "SparseMatrixRecommender", [AAp3], in Python

This implementation closely follows the:

• OOP implementation "ML-SparseMatrixRecommender", [AAp4], in Raku

• OOP implementation "SparseMatrixRecommender", [AAp5], in Swift

Instead of "monads" the implementations in this package and [AAp3] use OOP classes. Instead of "monadic pipelines" method chaining is used.

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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, Monadic Sparse Matrix Recommender Mathematica package, (2018), GitHub/antononcube.

[AAp2] Anton Antonov, Sparse Matrix Recommender Monad R package, (2018), R-packages at GitHub/antononcube.

[AAp3] Anton Antonov, SparseMatrixRecommender Python package, (2021), Python-packages at GitHub/antononcube.

[AAp4] Anton Antonov, ML::StreamsBlendingRecommender Raku package, (2021), GitHub/antononcube.

[AAp5] Anton Antonov, StreamsBlendingRecommender Swift package, (2022), GitHub/antononcube.