A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes
Studying food recipes is indispensable to understand the science of cooking. An essential problem in food computing is the adaptation of recipes to user needs and preferences. The main difficulty when adapting recipes is in determining ingredients relations, which are compound and hard to interpret....
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9351987/ |
| _version_ | 1819276888210669568 |
|---|---|
| author | Andrea Morales-Garzon Juan Gomez-Romero Maria J. Martin-Bautista |
| author_facet | Andrea Morales-Garzon Juan Gomez-Romero Maria J. Martin-Bautista |
| author_sort | Andrea Morales-Garzon |
| collection | DOAJ |
| description | Studying food recipes is indispensable to understand the science of cooking. An essential problem in food computing is the adaptation of recipes to user needs and preferences. The main difficulty when adapting recipes is in determining ingredients relations, which are compound and hard to interpret. Word embedding models can catch the semantics of food items in a recipe, helping to understand how ingredients are combined and substituted. In this work, we propose an unsupervised method for adapting ingredient recipes to user preferences. To learn food representations and relations, we create and apply a specific-domain word embedding model. In contrast to previous works, we not only use the list of ingredients to train the model but also the cooking instructions. We enrich the ingredient data by mapping them to a nutrition database to guide the adaptation and find ingredient substitutes. We performed three different kinds of recipe adaptation based on nutrition preferences, adapting to similar ingredients, and vegetarian and vegan diet restrictions. With a 95% of confidence, our method can obtain quality adapted recipes without a previous knowledge extraction on the recipe adaptation domain. Our results confirm the potential of using a specific-domain semantic model to tackle the recipe adaptation task. |
| first_indexed | 2024-12-23T23:47:22Z |
| format | Article |
| id | doaj.art-721c0eae02354ba4bde036152826e736 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:47:22Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-721c0eae02354ba4bde036152826e7362022-12-21T17:25:29ZengIEEEIEEE Access2169-35362021-01-019273892740410.1109/ACCESS.2021.30585599351987A Word Embedding-Based Method for Unsupervised Adaptation of Cooking RecipesAndrea Morales-Garzon0https://orcid.org/0000-0002-3458-0694Juan Gomez-Romero1https://orcid.org/0000-0003-0439-3692Maria J. Martin-Bautista2Department of Computer Science and Artificial Intelligence, University of Granada, Granada, SpainDepartment of Computer Science and Artificial Intelligence, University of Granada, Granada, SpainDepartment of Computer Science and Artificial Intelligence, University of Granada, Granada, SpainStudying food recipes is indispensable to understand the science of cooking. An essential problem in food computing is the adaptation of recipes to user needs and preferences. The main difficulty when adapting recipes is in determining ingredients relations, which are compound and hard to interpret. Word embedding models can catch the semantics of food items in a recipe, helping to understand how ingredients are combined and substituted. In this work, we propose an unsupervised method for adapting ingredient recipes to user preferences. To learn food representations and relations, we create and apply a specific-domain word embedding model. In contrast to previous works, we not only use the list of ingredients to train the model but also the cooking instructions. We enrich the ingredient data by mapping them to a nutrition database to guide the adaptation and find ingredient substitutes. We performed three different kinds of recipe adaptation based on nutrition preferences, adapting to similar ingredients, and vegetarian and vegan diet restrictions. With a 95% of confidence, our method can obtain quality adapted recipes without a previous knowledge extraction on the recipe adaptation domain. Our results confirm the potential of using a specific-domain semantic model to tackle the recipe adaptation task.https://ieeexplore.ieee.org/document/9351987/Data mappingfood computingnatural language processingrecipe adaptationword embedding |
| spellingShingle | Andrea Morales-Garzon Juan Gomez-Romero Maria J. Martin-Bautista A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes IEEE Access Data mapping food computing natural language processing recipe adaptation word embedding |
| title | A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes |
| title_full | A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes |
| title_fullStr | A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes |
| title_full_unstemmed | A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes |
| title_short | A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes |
| title_sort | word embedding based method for unsupervised adaptation of cooking recipes |
| topic | Data mapping food computing natural language processing recipe adaptation word embedding |
| url | https://ieeexplore.ieee.org/document/9351987/ |
| work_keys_str_mv | AT andreamoralesgarzon awordembeddingbasedmethodforunsupervisedadaptationofcookingrecipes AT juangomezromero awordembeddingbasedmethodforunsupervisedadaptationofcookingrecipes AT mariajmartinbautista awordembeddingbasedmethodforunsupervisedadaptationofcookingrecipes AT andreamoralesgarzon wordembeddingbasedmethodforunsupervisedadaptationofcookingrecipes AT juangomezromero wordembeddingbasedmethodforunsupervisedadaptationofcookingrecipes AT mariajmartinbautista wordembeddingbasedmethodforunsupervisedadaptationofcookingrecipes |