  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.
