Intelligent Food Recipes Recommendation System

Student: Tarila Sharon Yinkere (Project, 2025)
Department of Computer Science
University of Port-Harcourt, Rivers State

Abstract

The project focuses on developing an Intelligent Food Recipes Recommendation System, aimed at assisting users in discovering new recipes based on their preferences, dietary restrictions, and available ingredients. The system utilizes a combination of machine learning algorithms, Object-Oriented System Analysis and Design Methodology (OOSADM), and collaborative filtering techniques to analyze user input and suggest personalized recipes. Using Object-Oriented System Analysis and Design (OOSAD) methodology, the system architecture is carefully crafted to support modularity, reusability and scalability. The collaborative filtering approach enables the system to analyze user preferences and behaviors, using data from similar users to recommend recipes that align with individual tastes and dietary requirements. Python serves as the primary programming language, chosen for its versatility and rich ecosystem of libraries suitable for data processing, machine learning, and recommendation system development. This recommendation system aims to enhance user satisfaction by delivering relevant recipes, with potential applications in personalized nutrition, meal planning, and culinary exploration.

Keywords
intelligent recipes recommendation system
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