recommender-als: Recommendations using alternating least squares algorithm

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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.

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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:0.2.2.0
Executables movielens
Downloads 722 total (8 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2024-10-20 [all 1 reports]