W. Hasan, Kimia Tuz Zaman, Maryam Sadat Amiri Tehrani Zadeh, Juan Li
{"title":"Eat This, Not That! – a Personalised Restaurant Menu Decoder That Helps You Pick the Right Food","authors":"W. Hasan, Kimia Tuz Zaman, Maryam Sadat Amiri Tehrani Zadeh, Juan Li","doi":"10.1109/HealthCom54947.2022.9982770","DOIUrl":null,"url":null,"abstract":"Picking the right food from a restaurant menu sometimes is not an easy thing for many people: visitors who are not familiar with local restaurants' meal names and their ingredients, people with religious diet constraints, patients with nutrition requirements, and people with special diet preferences. It is not easy for these diners to choose meals from restaurant menus as they do not provide enough information for the diners to make decisions in a brief period. In this paper, we propose an AI-empowered personalized restaurant menu decoder app that can help users make wise choices from any menu in any restaurant. With an easy-to-use interface, the app can quickly rank the restaurant's menu items based on the user’s preferences and concerns. Preliminary test results have demonstrated the good usability of the proposed system.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HealthCom54947.2022.9982770","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Picking the right food from a restaurant menu sometimes is not an easy thing for many people: visitors who are not familiar with local restaurants' meal names and their ingredients, people with religious diet constraints, patients with nutrition requirements, and people with special diet preferences. It is not easy for these diners to choose meals from restaurant menus as they do not provide enough information for the diners to make decisions in a brief period. In this paper, we propose an AI-empowered personalized restaurant menu decoder app that can help users make wise choices from any menu in any restaurant. With an easy-to-use interface, the app can quickly rank the restaurant's menu items based on the user’s preferences and concerns. Preliminary test results have demonstrated the good usability of the proposed system.