# SweetRS: Dataset for a recommender systems of sweets

arXiv: https://arxiv.org/abs/1709.03496 · HTML: https://mlsanity.com/paper/1709.03496/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Introducing the Huber mechanism for differentially private low-rank matrix completion](https://mlsanity.com/paper/2206.07910/) (2022)
- [Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen](https://mlsanity.com/paper/2208.12356/) (2022)

## Abstract (arXiv)

Benchmarking recommender system and matrix completion algorithms could be greatly simplified if the entire matrix was known. We built a urlsweetrs.org platform with 77 candies and sweets to rank. Over 2000 users submitted over 44000 grades resulting in a matrix with 28% coverage. In this report, we give the full description of the environment and we benchmark the textscSoft-Impute algorithm on the dataset.