name: recommender-als version: 0.2.0.0 synopsis: Recommendations using alternating least squares algorithm description: This package provides a recommendation algorithm based on alternating least squares algorithm, as made famous by the Netflix Prize. . It takes as its input a list of user-item pairs and returns a list of recommendations for each user. The current implementation is limited to using unrated pairs. . The algorithm is parallelized and should be quick enough to train the model within seconds for a few thousand users and items. Getting recommendations from a computed model happens nearly instantly. . For implementation details, see "Large-scale Parallel Collaborative Filtering for the Netflix Prize" by Yunhong Zhou, Dennis Wilkinson, Robert Schreiber and Rong Pan. homepage: https://gitlab.com/kaol/recommender-als license: BSD3 license-file: LICENSE author: Kari Pahula maintainer: kaol@iki.fi copyright: Kari Pahula 2020 category: Numeric build-type: Simple extra-source-files: ChangeLog.md cabal-version: >=1.10 library exposed-modules: Numeric.Recommender.ALS build-depends: base >= 4.11 && < 5, containers >= 0.5 && < 1, data-default-class >= 0.1.2 && < 1, hmatrix >= 0.20 && < 1, parallel >= 3.2 && < 4, random >= 1.1 && < 2, vector >= 0.11 && < 1 hs-source-dirs: src default-language: Haskell2010