Classification of Cuisines from Sequentially Structured Recipes

• URL: http://arxiv.org/abs/2004.14165v1
• Date: Sun, 26 Apr 2020 05:40:36 GMT
• Title: Classification of Cuisines from Sequentially Structured Recipes
• Authors: Tript Sharma, Utkarsh Upadhyay and Ganesh Bagler
• Abstract summary: classification of cuisines based on their culinary features is an outstanding problem. We have implemented a range of classification techniques by accounting for this information on the RecipeDB dataset. The state-of-the-art RoBERTa model presented the highest accuracy of 73.30% among a range of classification models.
• Score: 8.696042114987966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cultures across the world are distinguished by the idiosyncratic patterns in their cuisines. These cuisines are characterized in terms of their substructures such as ingredients, cooking processes and utensils. A complex fusion of these substructures intrinsic to a region defines the identity of a cuisine. Accurate classification of cuisines based on their culinary features is an outstanding problem and has hitherto been attempted to solve by accounting for ingredients of a recipe as features. Previous studies have attempted cuisine classification by using unstructured recipes without accounting for details of cooking techniques. In reality, the cooking processes/techniques and their order are highly significant for the recipe's structure and hence for its classification. In this article, we have implemented a range of classification techniques by accounting for this information on the RecipeDB dataset containing sequential data on recipes. The state-of-the-art RoBERTa model presented the highest accuracy of 73.30% among a range of classification models from Logistic Regression and Naive Bayes to LSTMs and Transformers.

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