Nutrient Recommendation System for Personalized Diet


Authors : D. S. L. Manikanteswari; Md. Abubakar Siddiq; G. R. V. Phani Varma; B. Vijay; K. Rishik Reddy; A.C Naga Sai

Volume/Issue : Volume 10 - 2025, Issue 3 - March


Google Scholar : https://tinyurl.com/54zt8p4t

Scribd : https://tinyurl.com/ypk87r39

DOI : https://doi.org/10.38124/ijisrt/25mar1573

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Abstract : The Personalized Diet Nutrient Recommendation System is a novel solution that combines advanced AI agents and generative AI to provide diet suggestions that are adaptive and culturally relevant. The system takes into account a range of user information, including age, sex, weight, diet aims, activity levels, allergies, and geographical location preferences, to create dynamic meal plans. The project combines real-time calorie tracking with personalized exercise suggestions, taking a more holistic approach than traditional systems. The installation, methodology, and performance of the system in delivering personalized nutritional suggestions are described in this paper.

Keywords : Machine Learning, AI Agents, Generative AI, Personalized Diet, and Nutrient Recommendations.

References :

  1. Asst Prof. Mrs. D. Navya Narayana Kumari, T. Praveen Satya, B. Manikanta, A. Phani Chandana, Y. L.S Aditya. Diet Recommendation System Using Machine Learning. IJERT -February 2024.
  2. Vijay Jaiswal. A New Approach for Recommending Healthy Diet Using Predictive Data Mining Algorithm. IJRAJ-March 2019.
  3. Butti Gouthami, Malige Gangappa. A Nutritional Diet Recommendation System Using User Interest. IJARET-2020.
  4. Rachel Yera Toledo, Ahmad A. Alzahrani, Luis Martinez. A Food Recommendation System Based on Nutritional Information and User Preferences. IEEE-July 2019.

The Personalized Diet Nutrient Recommendation System is a novel solution that combines advanced AI agents and generative AI to provide diet suggestions that are adaptive and culturally relevant. The system takes into account a range of user information, including age, sex, weight, diet aims, activity levels, allergies, and geographical location preferences, to create dynamic meal plans. The project combines real-time calorie tracking with personalized exercise suggestions, taking a more holistic approach than traditional systems. The installation, methodology, and performance of the system in delivering personalized nutritional suggestions are described in this paper.

Keywords : Machine Learning, AI Agents, Generative AI, Personalized Diet, and Nutrient Recommendations.

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