We opensource under CC BY 4.0 license Lib-SibGMU - a university library circulation dataset - for a wide research community, and benchmark major algorithms for recommender systems on this dataset. For a recommender architecture that consists of a vectorizer that turns the history of the books borrowed into a vector, and a neighborhood-based recommender, trained separately, we show that using the fastText model as a vectorizer delivers competitive results.
@article{arxiv.2208.12356,
title = {Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen},
author = {Eduard Zubchuk and Mikhail Arhipkin and Dmitry Menshikov and Aleksandr Karaush and Nikolay Mikhaylovskiy},
journal= {arXiv preprint arXiv:2208.12356},
year = {2023}
}