Recipe Recommender System Using BERTopic Modelling Technique

Human civilization has been significantly impacted by technology, ushering in an era of tremendous growth. From the dawn of agriculture to the digital era, technical advancements have transformed communication, transportation, healthcare, and other fields. These advancements have not only improved the quality of life, but have also created new ethical and environmental issues, emphasising the intricate interplay between technology and society in our continual journey of development and adaptation. mRecipe Recommender Systems is based on user preferences to provide personalised culinary recommendations. Individual tastes, dietary limitations, and accessible ingredients are analysed by these algorithms to develop personalised meals. The use of such technologies not only improves convenience, but also encourages innovation in the kitchen, encouraging healthier, more sustainable, and pleasant cooking experiences. Interchanges and variations in the recipes are increasing because of ethnic variety, nutritional choices, and health requirements. Globalization and health-conscious dietary choices have necessitated recipes that accommodate a wide range of tastes, cultures, and dietary constraints. The internet era has simplified the access and sharing of varied culinary alternatives, expanding global culinary history. This study's Recipe Recommender uses data preprocessing, feature engineering, and collaborative filtering techniques to use a collection of various recipes and user interactions. This method allows the system to offer meals based on the user's preferences and dietary constraints, boosting the user's culinary experience. This study demonstrates how combining these technologies may revolutionise the art of cooking by providing users with a more pleasurable, personalised, and data-driven culinary experience.