AI-powered dining: text information extraction and machine learning for personalized menu recommendations and food allergy management

Abstract

Individuals with food allergies face limitations in social events and restaurant dining. Artificial intelligence solutions should be offered to this category. In this paper, a recommender system is proposed for the benefit of people with food allergies. The system aims to identify convenient options for the user in a restaurant/hotel menu. The system collects user’s allergy information and the restaurant menu, it extracts dishes names using a machine learning model. Then it conducts search about the recipes of these dishes and identify allergen-free ones. The system has been implemented as a mobile application involving a Naïve Bayes classification model and a web search API. The performance of the classifier was significant (accuracy 87%). Yet, an enhancement approach was introduced to increase the accuracy to 90%. In addition, an expert-driven test has been conducted and 98.5% of the system allergen identification was accurate in comparison with the original recipes used by restaurants’ chefs.