SweetRS: Dataset for a recommender systems of sweets
Published in
arXiv.org(2017)External Links:
TL
TL;DR
A benchmarking recommender system and matrix completion algorithms could be greatly simplified if the entire matrix was known, and the full description of the environment is given and the Soft-Impute algorithm is benchmarked.
Abstract
Benchmarking recommender system and matrix completion algorithms could be greatly simplified if the entire matrix was known. We built a \url{sweetrs.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 \textsc{Soft-Impute} algorithm on the dataset.
References2 items
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Spectral Regularization Algorithms for Learning Large Incomplete Matrices
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