Exploiting Food Embeddings for Ingredient Substitution

Identifying ingredient substitutes for cooking recipes can be beneficial for various goals, such as nutrient optimization or avoiding allergens. Natural language processing (NLP) techniques can be valuable tools to make use of the vast cooking-related knowledge available online, and aid in finding ingredient alternatives. Despite previous approaches to identify ingredient substitutes, there is still a lack of research in this area regarding the most recent developments in the field of NLP. On top of that, a lack of standardized evaluation metrics makes comparing approaches difficult. In this paper, we present two models for ingredient embeddings, Food2Vec and FoodBERT. In addition, we combine both approaches with images, resulting in two multimodal representation models. FoodBERT is furthermore used for relation extraction. We conduct a ground truth based evaluation for all approaches, as well as a human evaluation. The comparison shows that FoodBERT, and especially the multimodal version, is best suited for substitute recommendations in dietary use cases.

Automated summary

The study presents two ingredient embedding models, Food2Vec and FoodBERT, as well as two multimodal representation models. The research finds that FoodBERT is best suited for substitute recommendations in dietary use cases.