From Menus to the Interactive Food-Ordering Systems

Min-Ji Kim, Seong-Jin Park, Jaehwan Ha, Ju-Won Seo, Dinara Aliyeva, Kang-Min Kim

Abstract:

Conversational interfaces have emerged as an accessible and userfriendly alternative to traditional touch-based self-service kiosks
in food-ordering systems. Despite their promise, building such systems remains challenging due to the need for costly data annotation,
store-specific model adaptation, and scalable deployment. In this
study, we propose a fully automated, end-to-end framework that
transforms structured menu databases into high-quality annotated
datasets and efficiently deploys store-specific conversational models using a parameter-efficient fine-tuning method. Our approach
fine-tunes only 0.9% of the backbone model parameters per store,
enabling cost-effective and plug-and-play deployment across diverse environments. To enhance robustness, we further integrate
a recommendation module that suggests alternative items when
requested menu options are unavailable. Experimental results on
data from 27 stores in South Korea demonstrate that our framework
consistently outperforms existing data generation baselines in intent classification and slot filling performance, while maintaining
high annotation quality. Simulated real-world voice-ordering scenarios confirm the practicality of our framework for rapid, scalable,
and accessible deployment in real-world environments.