Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning

Helena Lee; Palakorn Achananuparp; Yue Liu; Ee Peng Lim; Lav R. Varshney

doi:10.1145/3357729.3357748

Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning

Helena Lee, Palakorn Achananuparp, Yue Liu, Ee Peng Lim, Lav R. Varshney

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels. However, laboratory analysis of dietary glycemic potency is time-consuming and expensive. In this paper, we explore a data-driven approach utilizing online crowdsourcing and machine learning to estimate the glycemic impact of cooking recipes. We show that a commonly used healthiness metric may not always be effective in determining recipes suitable for diabetics, thus emphasizing the importance of the glycemic-impact estimation task. Our best classification model, trained on nutritional and crowdsourced data obtained from Amazon Mechanical Turk (AMT), can accurately identify recipes which are unhealthful for diabetics.

Original language	English (US)
Title of host publication	DPH 2019 - Proceedings of the 9th International Conference on Digital Public Health
Publisher	Association for Computing Machinery
Pages	31-35
Number of pages	5
ISBN (Electronic)	9781450372084
DOIs	https://doi.org/10.1145/3357729.3357748
State	Published - Nov 20 2019
Event	9th International Conference on Digital Public Health, DPH 2019 - Marseille, France Duration: Nov 20 2019 → Nov 23 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	9th International Conference on Digital Public Health, DPH 2019
Country	France
City	Marseille
Period	11/20/19 → 11/23/19

Keywords

Glycemic Impact
Recipe Classification
Recipe Embeddings

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3357729.3357748

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Cite this

Lee, H., Achananuparp, P., Liu, Y., Lim, E. P., & Varshney, L. R. (2019). Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning. In DPH 2019 - Proceedings of the 9th International Conference on Digital Public Health (pp. 31-35). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3357729.3357748

Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning. / Lee, Helena; Achananuparp, Palakorn; Liu, Yue; Lim, Ee Peng; Varshney, Lav R.

DPH 2019 - Proceedings of the 9th International Conference on Digital Public Health. Association for Computing Machinery, 2019. p. 31-35 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, H, Achananuparp, P, Liu, Y, Lim, EP & Varshney, LR 2019, Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning. in DPH 2019 - Proceedings of the 9th International Conference on Digital Public Health. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 31-35, 9th International Conference on Digital Public Health, DPH 2019, Marseille, France, 11/20/19. https://doi.org/10.1145/3357729.3357748

Lee H, Achananuparp P, Liu Y, Lim EP, Varshney LR. Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning. In DPH 2019 - Proceedings of the 9th International Conference on Digital Public Health. Association for Computing Machinery. 2019. p. 31-35. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3357729.3357748

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abstract = "Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels. However, laboratory analysis of dietary glycemic potency is time-consuming and expensive. In this paper, we explore a data-driven approach utilizing online crowdsourcing and machine learning to estimate the glycemic impact of cooking recipes. We show that a commonly used healthiness metric may not always be effective in determining recipes suitable for diabetics, thus emphasizing the importance of the glycemic-impact estimation task. Our best classification model, trained on nutritional and crowdsourced data obtained from Amazon Mechanical Turk (AMT), can accurately identify recipes which are unhealthful for diabetics.",

keywords = "Glycemic Impact, Recipe Classification, Recipe Embeddings",

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note = "Funding Information: This research is supported by the National Research Foundation, Prime Minister{\textquoteright}s Office, Singapore under its International Research Centres in Singapore Funding Initiative.; 9th International Conference on Digital Public Health, DPH 2019 ; Conference date: 20-11-2019 Through 23-11-2019",

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