DEVELOPMENT OF A CONTENT-BASED DIET RECOMMENDATION MODEL

This study presents the development of a content-based dietary recommendation system that integrates hydration analysis and allergen management into personalised nutrition. The proposed model employs content-based filtering techniques to generate tailored recommendations, addressing dietary restrictions, hydration needs, and user preferences. A dataset of 1,001 food items was curated and preprocessed to include attributes such as water content, allergens, and nutritional values. The system’s performance was evaluated using precision, recall, F1-score, and simulated user feedback, achieving an F1-score of 82%. While results demonstrate the system’s ability to generate relevant, safe, diverse, and hydration-specific recommendations, limitations related to dataset diversity and the absence of dynamic feedback mechanisms present opportunities for further improvement. This study advances personalised nutrition by offering a holistic approach to dietary management, suitable for individual and clinical applications.