Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen
This provides a new dataset for the recommender systems research community, but the method is incremental.
The authors introduced Lib-SibGMU, a university library circulation dataset, and benchmarked recommender system algorithms on it, showing that using fastText as a vectorizer yields competitive results.
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.