Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen

2022/08/25 by Zubchuk, Eduard, Arhipkin, Mikhail, Menshikov, Dmitry +2
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Retrieval (cs.IR)

paper · doi:10.48550/arxiv.2208.12356

Abstract

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.

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