Installation
About
Provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) doi:10.48550/arXiv.2205.12371.
| Citation | recommenderlab citation info |
| github.com/mhahsler/recommenderlab | |
| Copyright | (C) Michael Hahsler |
| Bug report | File report |
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Maintainer
| Maintainer | Michael Hahsler |