TITLE: Predefined domain specific embeddings of food concepts and recipes: A case study on heterogeneous recipe datasets 

ABSTRACT: Although recipe data are very easy to come by nowadays, it is really hard to
find a complete recipe dataset - with a list of ingredients, nutrient values
per ingredient, and per recipe, allergens, etc. Recipe datasets are usually
collected from social media websites where users post and publish recipes.
Usually written with little to no structure, using both standardized and
non-standardized units of measurement. We collect six different recipe
datasets, publicly available, in different formats, and some including data in
different languages. Bringing all of these datasets to the needed format for
applying a machine learning (ML) pipeline for nutrient prediction [1], [2],
includes data normalization using dictionary-based named entity recognition
(NER), rule-based NER, as well as conversions using external domain-specific
resources. From the list of ingredients, domain-specific embeddings are created
using the same embedding space for all recipes - one ingredient dataset is
generated. The result from this normalization process is two corpora - one with
predefined ingredient embeddings and one with predefined recipe embeddings. On
all six recipe datasets, the ML pipeline is evaluated. The results from this
use case also confirm that the embeddings merged using the domain heuristic
yield better results than the baselines.
Although recipe data are very easy to come by nowadays, it is really hard to
find a complete recipe dataset - with a list of ingredients, nutrient values
per ingredient, and per recipe, allergens, etc. Recipe datasets are usually
collected from social media websites where users post and publish recipes.
Usually written with little to no structure, using both standardized and
non-standardized units of measurement. We collect six different recipe
datasets, publicly available, in different formats, and some including data in
different languages. Bringing all of these datasets to the needed format for
applying a machine learning (ML) pipeline for nutrient prediction [1], [2],
includes data normalization using dictionary-based named entity recognition
(NER), rule-based NER, as well as conversions using external domain-specific
resources. From the list of ingredients, domain-specific embeddings are created
using the same embedding space for all recipes - one ingredient dataset is
generated. The result from this normalization process is two corpora - one with
predefined ingredient embeddings and one with predefined recipe embeddings. On
all six recipe datasets, the ML pipeline is evaluated. The results from this
use case also confirm that the embeddings merged using the domain heuristic
yield better results than the baselines.
Predefined domain specific embeddings of food concepts and recipes: A case study on heterogeneous recipe datasets
http://arxiv.org/abs/2302.01005v1