Recipes Recommendation System using Machine Learning

Abstract: With the increasing popularity of online cooking platforms and the vast availability of recipe data, personalized recipe recommendation systems have become an essential tool to enhance user experience. This research presents a Machine Learning-based Recipe Recommender System that suggests the top five most relevant recipes based on user-provided ingredients or a recipe name. The system leverages Natural Language Processing (NLP) techniques to extract and analyses key features from a large recipe dataset, including ingredient lists, recipe titles, and preparation steps. A content-based filtering approach, enhanced by vectorization techniques such as TF-IDF and cosine similarity, is used to find recipes most like the user's input. Our model effectively narrows down recipe options by matching user preferences with existing recipes, providing personalized and efficient culinary suggestions. The proposed system demonstrates high accuracy in aligning with user intent and offers a scalable solution for integration into cooking apps or digital kitchen assistants