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.
|Versions [RSS] [faq]||0.1.0.0, 0.2.0.0, 0.2.1.0, 0.2.1.1|
|Dependencies||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) [details]|
|Copyright||Kari Pahula 2020|
|Uploaded||by kaol at 2020-08-26T10:58:29Z|
|Downloads||357 total (27 in the last 30 days)|
|Rating||(no votes yet) [estimated by Bayesian average]|
Docs available [build log]
Last success reported on 2020-08-26 [all 1 reports]