Citation

Ter, Zheng Bin and Palanichamy, Naveen and J, Jayapradha (2025) Generative AI-based Meal Recommender System. Journal of Informatics and Web Engineering, 4 (2). pp. 315-338. ISSN 2821-370X

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Abstract

Maintaining a balanced diet is essential for overall well-being, yet many individuals face challenges in meal planning due to time constraints, limited nutritional knowledge, and difficulty aligning meals with personal dietary needs. Traditional meal recommender systems often rely on predefined plans or collaborative filtering techniques, limiting their adaptability and personalization. This study presents a generative AI-based Meal Recommender System utilizing Variational Autoencoders (VAEs) to generate personalized and nutritionally balanced meal plans. The system processes user inputs, such as dietary preferences, nutritional goals, and ingredient availability, to provide tailored recommendations. VAEs effectively uncover hidden dietary patterns and nutritional relationships within complex data, facilitating relevant and personalized meal suggestions. The system is trained and evaluated using two integrated datasets: one containing detailed nutritional information for complete meal plans,including attributes such as calories, protein, fats, carbohydrates, and sodium, and another listing individual dishes along with their names and user ratings. The meal plan dataset connects multiple dishes into structured daily meal schedules, while the dish dataset provides popularity and quality insights through user feedback. Together, these datasets enable the generation of personalized andnutritionally optimized meal recommendations. Experimental evaluation indicates strong ranking performance with a Normalized Discounted Cumulative Gain (NDCG) score of 0.963. However, Root Mean Square Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) scores of 47.77, 2282.32, and 36.28, respectively, highlight potential areas for improving nutritional accuracy. A practical comparison with existing meal recommendation applications demonstrates the VAE model’s advantages in terms of personalization, nutritional fine-tuning, and recommendation diversity. The research contributes to AI-driven nutrition planning, healthcare, and fitness, offering a scalable and intelligent solution for personalized dietary recommendations.

Item Type:	Article
Uncontrolled Keywords:	Generative AI
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	25 Jun 2025 08:36
Last Modified:	25 Jun 2025 08:36
URII:	http://shdl.mmu.edu.my/id/eprint/14024

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