homepage: https://gitlab.com/kaol/recommender-als
changelog-type: markdown
hash: 49c8bbcce004b529ba891048b0119d0acd0fffef7f67a5b5772a2ea37cdaabce
test-bench-deps: {}
maintainer: kaol@iki.fi
synopsis: Recommendations using alternating least squares algorithm
changelog: |
  # Revision history for recommender-als

  ## 0.1.0.0 -- 2020-07-09

  * First version.
basic-deps:
  base: '>=4.11 && <5'
  data-default-class: '>=0.1.2 && <1'
  containers: '>=0.5 && <1'
  random: '>=1.1 && <2'
  parallel: '>=3.2 && <4'
  hmatrix: '>=0.20 && <1'
  vector: '>=0.11 && <1'
all-versions:
- 0.1.0.0
author: Kari Pahula
latest: 0.1.0.0
description-type: haddock
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.
license-name: BSD-3-Clause

Update from Hackage at 2020-07-09T07:51:15Z
Update from Hackage at 2020-07-09T07:51:15Z
