Creative Culinary Recipe Generation Based on Statistical Language Models
Many works have been done in an effort to create systems for automatic generation of creative culinary recipes. Although most of them are related to the recipe ingredient lists, few works have been done to evaluate and generate the preparation steps of culinary recipes. This work proposes the use of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9153554/ |
| _version_ | 1819275073240956928 |
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| author | Willian Antonio dos Santos Joao Ribeiro Bezerra Luis Fabricio Wanderley Goes Flavia Magalhaes Freitas Ferreira |
| author_facet | Willian Antonio dos Santos Joao Ribeiro Bezerra Luis Fabricio Wanderley Goes Flavia Magalhaes Freitas Ferreira |
| author_sort | Willian Antonio dos Santos |
| collection | DOAJ |
| description | Many works have been done in an effort to create systems for automatic generation of creative culinary recipes. Although most of them are related to the recipe ingredient lists, few works have been done to evaluate and generate the preparation steps of culinary recipes. This work proposes the use of statistical Language Models, as well as the perplexity metric, for the generation of culinary recipes. In this work, we also developed a system for automatic generation of creative culinary recipes using two approaches: one based on a genetic programming algorithm guided by the proposed language model; and the other based on a decomposition of existing recipes and recomposition of new recipes through a genetic algorithm guided by the proposed language model. This second approach achieved the best results. For this approach, a total of 6 recipes were generated to evaluate, through an online survey, the influence of the Language Model in the generation of recipes with better use of secondary ingredients, oils and seasonings, throughout the preparation steps. In the comparison between these two groups of recipes, the respondents considered the recipes generated using the language model as having the best quality, presenting an average evaluation of 63.6% of the scale (i.e. between medium and good use of oils and seasonings compared to recipes from the other group). In addition, a recipe from this approach was cooked and tasted for taste assessment, obtaining an average evaluation of 93% of the scale. |
| first_indexed | 2024-12-23T23:18:31Z |
| format | Article |
| id | doaj.art-a2660128626b4e51b6163053c84f54f7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:18:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a2660128626b4e51b6163053c84f54f72022-12-21T17:26:24ZengIEEEIEEE Access2169-35362020-01-01814626314628310.1109/ACCESS.2020.30134369153554Creative Culinary Recipe Generation Based on Statistical Language ModelsWillian Antonio dos Santos0https://orcid.org/0000-0003-0623-7714Joao Ribeiro Bezerra1Luis Fabricio Wanderley Goes2https://orcid.org/0000-0003-1801-9917Flavia Magalhaes Freitas Ferreira3Electrical Engineering Departament, Pontical Catholic University of Minas Gerais (PUC Minas), Belo Horizonte, BrazilComputer Science Department, Pontical Catholic University of Minas Gerais (PUC Minas), Belo Horizonte, BrazilComputer Science Department, Pontical Catholic University of Minas Gerais (PUC Minas), Belo Horizonte, BrazilElectrical Engineering Departament, Pontical Catholic University of Minas Gerais (PUC Minas), Belo Horizonte, BrazilMany works have been done in an effort to create systems for automatic generation of creative culinary recipes. Although most of them are related to the recipe ingredient lists, few works have been done to evaluate and generate the preparation steps of culinary recipes. This work proposes the use of statistical Language Models, as well as the perplexity metric, for the generation of culinary recipes. In this work, we also developed a system for automatic generation of creative culinary recipes using two approaches: one based on a genetic programming algorithm guided by the proposed language model; and the other based on a decomposition of existing recipes and recomposition of new recipes through a genetic algorithm guided by the proposed language model. This second approach achieved the best results. For this approach, a total of 6 recipes were generated to evaluate, through an online survey, the influence of the Language Model in the generation of recipes with better use of secondary ingredients, oils and seasonings, throughout the preparation steps. In the comparison between these two groups of recipes, the respondents considered the recipes generated using the language model as having the best quality, presenting an average evaluation of 63.6% of the scale (i.e. between medium and good use of oils and seasonings compared to recipes from the other group). In addition, a recipe from this approach was cooked and tasted for taste assessment, obtaining an average evaluation of 93% of the scale.https://ieeexplore.ieee.org/document/9153554/Language modelsculinary recipecomputational creativity |
| spellingShingle | Willian Antonio dos Santos Joao Ribeiro Bezerra Luis Fabricio Wanderley Goes Flavia Magalhaes Freitas Ferreira Creative Culinary Recipe Generation Based on Statistical Language Models IEEE Access Language models culinary recipe computational creativity |
| title | Creative Culinary Recipe Generation Based on Statistical Language Models |
| title_full | Creative Culinary Recipe Generation Based on Statistical Language Models |
| title_fullStr | Creative Culinary Recipe Generation Based on Statistical Language Models |
| title_full_unstemmed | Creative Culinary Recipe Generation Based on Statistical Language Models |
| title_short | Creative Culinary Recipe Generation Based on Statistical Language Models |
| title_sort | creative culinary recipe generation based on statistical language models |
| topic | Language models culinary recipe computational creativity |
| url | https://ieeexplore.ieee.org/document/9153554/ |
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