Abstract:
With growing diversity in personal food preference and regional cuisine style, personalized information systems that can transform a recipe into any selected regional cuisine style that a user might prefer would help food companies and professional chefs create new recipes. The aim of the study is to explore computational techniques which can be utilised in order to convert a recipe which belongs to a cuisine (Source cuisine) to another cuisine (Target cuisine) by changing one ingredient from the Original recipe. For ease of understanding we will call the starting recipe from the Source cuisine as Original recipe and the final recipe which belongs to the Target cuisine as the Transformed recipe. There are two major tasks that need to be done in order to change a recipe from one cuisine to another computationally. (1) Swap each ingredient of the Original recipe with the ingredients present in the database and, (2) Classify the recipe based on its ingredients and check which cuisine does the recipe belong to. The Dataset used mainly comprises of labeled corpus of Yummly Dataset recipes. We make use of different Machine Learning, natural Language Processing and Deep Learning techniques to achieve the aim of the study. In recent years, Travel and Tourism has flourished and different ethnicities have started to live in the same countries. Some people feel the need of fusion cuisines. Some people also love to cook their own food and customize the recipes to their need. These types of computational models and studies in the field of Computational Gastronomy are not only research fields, and can also be used to create new recipes and innovation in the food industry.
