Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen
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Abstract (arXiv)
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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References · 8
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2013From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviewscited by 107
- 2019A Survey on Session-based Recommender Systemscited by 97
- 2018Local Descriptors Optimized for Average Precisioncited by 43
- 2015Metadata Embeddings for User and Item Cold-start Recommendationscited by 29
- 2020HotelRec: a Novel Very Large-Scale Hotel Recommendation Datasetcited by 14
- 2017SweetRS: Dataset for a recommender systems of sweetscited by 2
- 2022Using a Language Model in a Kiosk Recommender System at Fast-Food Restaurantscited by 1
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