Nutrition Meets AI

This chapter examines the personalization of nutrition using AI-powered recommender systems, focusing on how they can improve health goals and encourage sustainable eating habits. It incorporates the latest developments in machine learning, including matrix factorization, convolutional neural networks, reinforcement learning, and graph neural networks in devising automated food identification, meal planning, and interactive dietary recommendations. The chapter describes how these systems, using biometric data like glucose levels and gut microbiome profiles, behavior analytics such as eating and preference patterns, and contextual information like time, location, and cultural understanding, provide personalized recommendations tailored to individuals' unique health profiles. The practical applications range from dealing with chronic illnesses and obesity to sensitive dieting, demonstrating their practicality. Actionable recommendations are derived from the comparative analysis of benchmark datasets Nutrition5k, MyFoodRepo, and FoodKG, as well as the models' performance evaluations.