recommender-als: Recommendations using alternating least squares algorithm
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.
Downloads
- recommender-als-0.2.2.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
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For package maintainers and hackage trustees
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| Versions [RSS] | 0.1.0.0, 0.2.0.0, 0.2.1.0, 0.2.1.1, 0.2.2.0 |
|---|---|
| Change log | ChangeLog.md |
| Dependencies | base (>=4.11 && <5), bytestring, cassava, containers (>=0.5 && <1), data-default-class (>=0.1.2 && <1), hmatrix (>=0.20 && <1), optparse-applicative, parallel (>=3.2 && <4), random (>=1.1 && <2), recommender-als, text, vector (>=0.11 && <1) [details] |
| License | BSD-3-Clause |
| Copyright | Kari Pahula 2020, 2024 |
| Author | Kari Pahula |
| Maintainer | kaol@iki.fi |
| Category | Numeric |
| Home page | https://gitlab.com/kaol/recommender-als |
| Uploaded | by kaol at 2024-10-13T13:17:10Z |
| Distributions | NixOS:0.2.1.1 |
| Executables | movielens |
| Downloads | 714 total (24 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
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| Status | Docs available [build log] Last success reported on 2024-10-20 [all 1 reports] |