SWAD: A New Machine Learning based Recipe Recommendation System

The Swad model streamlines meal preparation by offering customized recipe suggestions tailored to available ingredients, user preferences, and dietary requirements. The revolutionizes leveraging recipe artificial Swad discovery intelligence recommend meals based on user-input ingredients. Addressing the modern need for personalized and efficient culinary exploration, Swad integrates seamlessly into various platforms like smart kitchen appliances and cooking apps. The need for such a model arises in a generation seeking convenience, reduced food waste, and diversified culinary offering solutions experiences, that align sustainability and user personalization, Swad addresses gaps in existing systems. This system employs the Term Frequency Inverse Document Frequency model combined with cosinesimilarity to process textual inputs such as ingredients and cooking preferences. TF-IDF evaluates the importance of terms within a recipe database, while cosine similarity identifies the most relevant recipes by comparing user queries with database entries. This approach ensures tailored, recommendations and enhances user engagement. Swad demonstrates the model by to by with precise transformative potential of machine learning in everyday cooking. By reducing decision fatigue, promoting sustainability, and introducing users to global cuisines, it establishes itself as an indispensable tool for modern kitchens.