INTELLIGENT FOOD RECIPES RECOMMENDATION SYSTEM
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.
Full-Text Access Notice
In accordance with the NERD Policy on promoting peer-reviewed publication, public access to the full text of a project, thesis or dissertation is restricted for three years, allowing the author and supervisors sufficient time to pursue peer-reviewed publication.
During this period, researchers with legitimate academic or research purposes may request authorisation directly from the author to enable NERD to release the indexed full texts of the work using the form below.