mlsanityData to 7 Oct 2026

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

arXiv 2202.04145 ↗First posted 8 Feb 2022cs.AI cs.IRcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 8 Feb 2022.

Builds on

4 of 18 references.

References · 4

  1. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  2. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 994
  3. 2018Local Descriptors Optimized for Average Precisioncited by 43
  4. 2020Context-Aware Drive-thru Recommendation Service at Fast Food Restaurantscited by 2

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Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmencited by 0