# Using a Language Model in a Kiosk Recommender System at Fast-Food Restaurants

arXiv: https://arxiv.org/abs/2202.04145 · HTML: https://mlsanity.com/paper/2202.04145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Local Descriptors Optimized for Average Precision](https://mlsanity.com/paper/1804.05312/) (2018)
- [Context-Aware Drive-thru Recommendation Service at Fast Food Restaurants](https://mlsanity.com/paper/2010.06197/) (2020)

## Built on by (most cited first, 1 of 1)

- [Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen](https://mlsanity.com/paper/2208.12356/) (2022)

## Abstract (arXiv)

Kiosks are a popular self-service option in many fast-food restaurants, they save time for the visitors and save labor for the fast-food chains. In this paper, we propose an effective design of a kiosk shopping cart recommender system that combines a language model as a vectorizer and a neural network-based classifier. The model performs better than other models in offline tests and exhibits performance comparable to the best models in A/B/C tests.