DEVELOPMENT OF A RULE-BASED SYSTEM FOR PERSONALIZED FOOD RECOMMENDATION IN THE NIGERIAN CONTEXT.
Abstract
In a culturally diverse country like Nigeria, food preferences and dietary restrictions vary
significantly across regions, health conditions, and personal beliefs. However, most existing
food recommendation systems are designed for Western users and fail to reflect the complexity
of Nigerian cuisine. This project presents the design and implementation of a rule-based
personalized food recommendation system tailored to the Nigerian context.
The system allows users to input personal details such as food allergies, health conditions for
example, diabetes, ulcer, etc., preferences for example, vegetarian, spicy, etc., and selected
mealtime (breakfast, lunch, or dinner). Based on these inputs, a rule-based inference engine
evaluates user data against a structured database of Nigerian dishes and applies expert-defined
IF–THEN rules to suggest culturally appropriate and health-conscious meals. Developed using
React.js for the frontend, Express.js for the backend, and PostgreSQL for data storage, the
system is lightweight, transparent, and suitable for low-resource environments.
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