Lib-sibgmu -- A University Library Circulation Dataset For Recommender Systems Developmen
Eduard Zubchuk, Mikhail Arhipkin, Dmitry Menshikov, Aleksandr Karaush, Nikolay Mikhaylovskiy . Arxiv 2022 – 0 citations
[Paper]
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.