Journal of Artificial Intelligence Research & Advances Review Article
Recipe-Fusion: Multimodal Food Recipe Recommendation System
Abstract
The food recipe recommendation system using data science is a software solution designed to help users discover new and delicious food options based on their food history and other relevant data. This system recommends various recipes based on the input given by the user and it helps to filter out the recipes on course type, diet type, and nature of the food (including non-veg, and veg) using a recommendation technique. The system also analyzes food trends and popular dishes that the users might not have considered before. The system can recognize the input ingredients provided by the user and give the recipe based on the ingredients mentioned. Various machine learning and deep learning approaches like convolutional neural networks (CNN) and recurrent neural networks (RNN) will be used for the implementation of this system. The CNN model is trained and implemented to recommend the recipes based on the image similarity score. The RNN model is trained and implemented for the prediction of the recipes from the input (recipe name) given by the user. Both models (CNN and RNN) are used simultaneously for the implementation of the system, which is why it is named as "Multimodal Food Recipe Recommendation System". This system will help the person learn and make recipes based on the input given to it. This system will simplify finding relevant recipes for the user.
Keywords
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