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

被引：0
作者：
Samiha Brahimi [1 ]
机构：
[1] Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam
来源：
International Journal of Information Technology | 2025年 / 17卷 / 4期
关键词：
Information retrieval; Machine learning; Menu recommendation; Recommender systems;
D O I：
10.1007/s41870-024-02154-9
中图分类号：
学科分类号：
摘要：
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. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.
引用
收藏
页码：2107 / 2115
页数：8
相关论文
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