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Classification of Cuisines from Sequentially Structured Recipes

机译：从结构化食谱中对美食进行分类

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摘要

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.
机译：世界各地的文化都以其烹饪方式的独特性而著称。这些美食的特征在于其子结构，例如配料，烹饪过程和器皿。这些区域固有的子结构的复杂融合定义了美食的身份。基于烹饪特征对美食进行准确分类是一个突出的问题，并且迄今为止，已经尝试通过将食谱的成分考虑为特征来解决。先前的研究尝试通过使用非结构化食谱来对美食进行分类，而不考虑烹饪技术的细节。实际上，烹饪过程/技术及其顺序对于食谱的结构及其分类非常重要。在本文中，我们通过考虑RecipeDB数据集（包含配方上的顺序数据）上的此信息，实现了一系列分类技术。最新的RoBERTa模型在从Logistic回归和朴素贝叶斯到LSTM和变压器的一系列分类模型中，提供了73.30％的最高准确性。

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