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

arXiv: https://arxiv.org/abs/2208.12356 · HTML: https://mlsanity.com/paper/2208.12356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 61 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews](https://mlsanity.com/paper/1303.4402/) (2013)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Local Descriptors Optimized for Average Precision](https://mlsanity.com/paper/1804.05312/) (2018)
- [Metadata Embeddings for User and Item Cold-start Recommendations](https://mlsanity.com/paper/1507.08439/) (2015)
- [HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset](https://mlsanity.com/paper/2002.06854/) (2020)
- [SweetRS: Dataset for a recommender systems of sweets](https://mlsanity.com/paper/1709.03496/) (2017)
- [Using a Language Model in a Kiosk Recommender System at Fast-Food Restaurants](https://mlsanity.com/paper/2202.04145/) (2022)

## 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.