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