The rapid growth of digital food ordering systems has transformed the way users interact with restaurant services. However, traditional systems rely heavily on static menu interfaces, requiring users to manually browse through available options. This approach is time-consuming and lacks personalization, leading to inefficient decision-making. This research presents CafeChatbot, an AI-powered conversational food ordering system that utilizes natural language processing and large language models to provide intelligent food recommendations. The system integrates the Google Gemini API to interpret user intent and generate context-aware suggestions based on menu data and user preferences. The proposed system consists of a React-based frontend, a Spring Boot backend, and a relational database for storing menu items and orders. It enables users to interact using natural language queries such as dietary preferences, nutritional requirements, and food choices. Experimental observations indicate that the system improves user experience, reduces ordering time, and enhances recommendation accuracy. This research demonstrates the effectiveness of conversational AI in transforming traditional food ordering systems into intelligent, user-centric platforms.
Gosain et al. (Tue,) studied this question.