TRANSFORMER-BASED INTELLIGENT SYSTEM FOR PERSONALIZED NUTRITION AND DYNAMIC DIET PLANNING

Ankita Rawat; Kirti Chaprana; Kshama; Tripti

doi:10.29121/granthaalayah.v11.i11.2023.6115

Authors

Ankita Rawat Computer Science & Engineering, Echelon Institute of Technology, Faridabad
Kirti Chaprana Computer Science & Engineering, Echelon Institute of Technology, Faridabad
Kshama Computer Science & Engineering, Echelon Institute of Technology, Faridabad
Tripti Computer Science & Engineering, Echelon Institute of Technology, Faridabad

DOI:

https://doi.org/10.29121/granthaalayah.v11.i11.2023.6115

Keywords:

Transformer, Intelligent System, Personalized, Dynamic, Nutrition, Diet, Food

Abstract [English]

Every day, individuals make numerous decisions related to food. Questions such as “What should I eat?”, “Where should I eat?”, “How much nutritional value does this food have?”, “Can this help me lose weight?”, and “Can this food improve my health?” are common in daily life. Given the complexities and variety of dietary choices, recommendation systems have emerged to assist users in making quicker, more informed decisions in this vast information space. These systems offer valuable content and services aimed at influencing user behavior by providing tailored recommendations.
In this project, we explore the potential of transformer-based models to enhance food and diet recommendations by incorporating nutritional data. The proposed system will use large datasets of user inputs to create personalized dietary and nutritional suggestions. These recommendations will be dynamic and adaptable, responding to real-time user feedback. The system will not only generate individualized diet plans but also modify its recommendations based on evolving user preferences, making nutrition and diet planning more flexible and responsive.
The primary goal of this project is to improve access to healthier food options and guide users in their dietary choices, supporting long-term health and well-being. By leveraging advanced machine learning techniques, particularly transformer models, we aim to significantly impact the nutrition domain and help individuals achieve their dietary goals more effectively and sustainably. Through this innovation, we aspire to foster healthier eating habits and contribute to better overall nutrition for individuals.

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