Paper Type
Complete
Paper Number
1877
Description
This study advances an innovative recipe recommender system that is designed to cater to the personalized needs and preferences of individuals with cardiovascular conditions. These factors encompass the user's health conditions, taste preferences, and the availability of ingredients in their refrigerators. Additionally, we incorporate social factors such as cooking frequency and recipe viewing, aiming to leverage the power of social influence in promoting recipe adoption. Furthermore, our system seamlessly integrates user interactions with the interface, including actions such as recipe like and favorite, to facilitate efficient recipe discovery. This integration aims to boost perceived behavioral control and alleviate the practical challenges associated with implementing recommended recipes. We conclude by presenting the design, implementation, and empirical results of our dynamic, personalized recommendation system.
Recommended Citation
Liu, Quanchen; Xiong, Bingqing; Cai, Zhao; Zhang, Pengzhu; and Tan, Chee-Wee, "Personalized and Dynamic Diet Recommendation for Individuals with Cardiovascular Diseases" (2024). PACIS 2024 Proceedings. 6.
https://aisel.aisnet.org/pacis2024/track04_dessci/track04_dessci/6
Personalized and Dynamic Diet Recommendation for Individuals with Cardiovascular Diseases
This study advances an innovative recipe recommender system that is designed to cater to the personalized needs and preferences of individuals with cardiovascular conditions. These factors encompass the user's health conditions, taste preferences, and the availability of ingredients in their refrigerators. Additionally, we incorporate social factors such as cooking frequency and recipe viewing, aiming to leverage the power of social influence in promoting recipe adoption. Furthermore, our system seamlessly integrates user interactions with the interface, including actions such as recipe like and favorite, to facilitate efficient recipe discovery. This integration aims to boost perceived behavioral control and alleviate the practical challenges associated with implementing recommended recipes. We conclude by presenting the design, implementation, and empirical results of our dynamic, personalized recommendation system.
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Comments
Design