@inproceedings{majumder-etal-2019-generating,
title = "Generating Personalized Recipes from Historical User Preferences",
author = "Majumder, Bodhisattwa Prasad and
Li, Shuyang and
Ni, Jianmo and
McAuley, Julian",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1613",
doi = "10.18653/v1/D19-1613",
pages = "5976--5982",
abstract = "Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user{'}s historical preferences. We attend on technique- and recipe-level representations of a user{'}s previously consumed recipes, fusing these {`}user-aware{'} representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model{'}s ability to generate plausible and personalized recipes compared to non-personalized baselines.",
}
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<abstract>Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user’s historical preferences. We attend on technique- and recipe-level representations of a user’s previously consumed recipes, fusing these ‘user-aware’ representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model’s ability to generate plausible and personalized recipes compared to non-personalized baselines.</abstract>
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%0 Conference Proceedings
%T Generating Personalized Recipes from Historical User Preferences
%A Majumder, Bodhisattwa Prasad
%A Li, Shuyang
%A Ni, Jianmo
%A McAuley, Julian
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F majumder-etal-2019-generating
%X Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user’s historical preferences. We attend on technique- and recipe-level representations of a user’s previously consumed recipes, fusing these ‘user-aware’ representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model’s ability to generate plausible and personalized recipes compared to non-personalized baselines.
%R 10.18653/v1/D19-1613
%U https://aclanthology.org/D19-1613
%U https://doi.org/10.18653/v1/D19-1613
%P 5976-5982
Markdown (Informal)
[Generating Personalized Recipes from Historical User Preferences](https://aclanthology.org/D19-1613) (Majumder et al., EMNLP-IJCNLP 2019)
ACL
- Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, and Julian McAuley. 2019. Generating Personalized Recipes from Historical User Preferences. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5976–5982, Hong Kong, China. Association for Computational Linguistics.