Evaluation of Cooking Recipes Using Their Texts and Images

Nonaka, Mei; Otake, Kohei; Namatame, Takashi

doi:10.1007/978-3-030-77626-8_21

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Related Work 21 Representation Learning For Recipes1

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2022

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“…Previous works that have introduced various deep learning approaches for improved representation learning of cooking recipes. Cross-modal or multimodal approaches incorporating recipe texts with images have been introduced where some focused on improving representation learning on recipes [6,8,32,33,37,48,50,53,54].…”

Section: Related Work 21 Representation Learning For Recipesmentioning

confidence: 99%

RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer

Gim

Choi

Maruyama

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed RecipeMind, a food anity score prediction model that quanties the suitability of adding an ingredient to set of other ingredients. We constructed a large-scale dataset containing ingredient co-occurrence based scores to train and evaluate RecipeMind on food anity score prediction. Deployed in recipe ideation, RecipeMind helps the user expand an initial set of ingredients by suggesting additional ingredients. Experiments and qualitative analysis show RecipeMind's potential in fullling its assistive role in cuisine domain.

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