Modelling Food and Mood Relation with Dynamic Personas

Abstract

Persona is not a static demographic label (“25-year-old student”) but an adaptive, evolving representation of the user. We propose a dynamic persona model that acts as a living mirror of the user, constantly adapting to their mental state, context, and behavior, and feeding that into food suggestions that support emotional wellbeing. The model combines relatively stable attributes (e.g., dietary preferences, allergies) with time-sensitive states (e.g., stress). We operationalize this through state charts and ontologies that link mental states with nutrition recommendations grounded in nutritional psychiatry. The resulting hybrid pipeline integrates ontological reasoning with adaptive learning to continuously update the state and recommend context-appropriate foods aimed at stabilizing or improving well-being. A proof-of-feasibility prototype demonstrates how state transitions can trigger timely adjustments in food suggestions without compromising nutritional adequacy and user constraints. This work positions dynamic personas as contextual twins that evolve with the user, enabling explainable and responsive food recommendations. The work also establishes the feasibility for integrating multimodal data streams from smart devices (e.g., wearables, smart kitchen tools, smart plates, smart fridges) to capture daily fluctuations relevant to mental health and link them semantically to food-related ontologies.