Automatic Recipe Ingredient Substitution Based on Text Mining and Data Clustering Approaches

With the advance of Information Technologies, recipe sharing websites have become very common, and people can use such systems in an attempt to find a recipe that fits both their desires and nutritional needs. But sometimes ingredients from a given recipe list may not be available, what may require some adaptations by replacing the missing ingredients. In this work, a data-driven approach is employed to develop a new recipe generation system, that recommends substitute ingredients to adapt recipes into a target domain. The proposed system, which is based on Text Mining and Data Clustering techniques, is evaluated by means of a qualitative analysis, showing promising results.