RecipeRadar: An AI-Powered Recipe Recommendation System

This paper presents the development of an advanced recipe recommendation system powered by natural language processing (NLP). To address the challenges posed by the overwhelming abundance of online recipes, this system provides personalized recipe suggestions based on user inputs, dietary preferences, and ingredient restrictions. The core of the system is a multi-input neural network model that processes and categorizes recipes using various textual inputs such as ingredients, recipe names, and descriptions. The model adeptly handles multi-label classification and multi-input data, two primary challenges in the domain of digital gastronomy. Extensive data preprocessing, including text normalization, tokenization, and vectorization, was conducted to prepare the dataset sourced from Kaggle. The model’s performance was rigorously evaluated through training and validation accuracy metrics, displaying its capability to categorize and recommend recipes effectively. The system’s potential applications span from personal cooking assistance to professional culinary platforms, highlighting its significance in enhancing digital culinary experiences. Future work includes expanding the dataset for greater diversity, integrating models that are more complex for improved NLP, and developing a user-centric mobile application. This study contributes to the growing field of AI in gastronomy, demonstrating the practical application of machine learning in everyday life domains.