文献类型：期刊文献

英文题名：Personalized and Adaptive Diet Recommendation for Individuals with Cardiovascular Diseases

作者：Liu, Quanchen[1,2]; Xiong, Bingqing[3]; Cai, Zhao[4]; Zhang, Hengrun[5]; Tan, Chee-Wee[6]; Liu, Xiaohui[7,8]

机构：[1] International Business School, Beijing Foreign Studies University, Beijing, China; [2] Department of Digitalization, Copenhagen Business School, Frederiksberg, Denmark; [3] Deakin Business School, Department of Information Systems and Business Analytics, Deakin University, Melbourne, Australia; [4] University of Nottingham Ningbo China, Nottingham University, Business School China, Ningbo, China; [5] School of Information Science and Engineering, Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China; [6] Faculty of Business, Department of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong; [7] Business School, Department of Artificial Intelligence, University of Shanghai for Science and Technology, School of Intelligent Emergency Management, Shanghai, China; [8] School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai, China

年份：2026

卷号：17

期号：2

外文期刊名：ACM Transactions on Management Information Systems

收录：EI(收录号：20262721044228);Scopus(收录号：2-s2.0-105043607231)

语种：英文

外文关键词：Behavioral research - Computation theory - Cooking - Food ingredients - Human computer interaction - Human engineering - Nutrition - Recommender systems - User interfaces

摘要：Given the profound impact of diet on cardiovascular health, appropriate dietary intake in home cooking is widely acknowledged as being instrumental in preventing cardiovascular diseases (CVDs). However, contemporary dietary recommendation systems for patients with CVDs are limited by population-level guidelines, static advice, as well as a lack of consideration for contextual conditions and individual preferences. We hence subscribe to the Theory of Planned Behavior (TPB) to design a novel recipe recommender system. Our proposed recipe recommender system takes into account users’ personal attitudes, subjective norms, and perceived behavioural control as focal considerations in recipe recommendations. Particularly, our recipe recommendation system assimilates personalized considerations—including health conditions, taste preferences, and available ingredients in the user's refrigerator—with social elements in the likes of cooking frequency, recipe viewing intensity, and user comments to bolster the acceptability of recommended recipes. Additionally, by incorporating user interactions in the likes of adding and liking recipes, our proposed recipe recommender system streamlines recipe discovery and strengthens users’ perceived behavioural control to maintain dietary choices. This integration aims to alleviate the practical challenges associated with adopting recommended recipes, rendering it much easier for users to adhere to dietary guidelines. Based on empirical validation, we not only deliver detailed insights into the implementation of a recipe recommendation system, but we also validate its utility in enhancing user engagement and satisfaction. ? 2026 Copyright held by the owner/author(s).