Using a Language Model in a Kiosk Recommender System at Fast-Food Restaurants
This work addresses the need for more efficient and effective self-service ordering systems for fast-food restaurant customers and chains, but it is incremental as it builds on existing language model and neural network techniques.
The authors tackled the problem of improving kiosk shopping cart recommendations in fast-food restaurants by combining a language model as a vectorizer with a neural network-based classifier, resulting in better performance than other models in offline tests and comparable performance to the best models in A/B/C tests.
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.