TY - GEN
T1 - Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning
AU - Lee, Helena
AU - Achananuparp, Palakorn
AU - Liu, Yue
AU - Lim, Ee Peng
AU - Varshney, Lav R.
N1 - Funding Information:
This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its International Research Centres in Singapore Funding Initiative.
Funding Information:
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11/20
Y1 - 2019/11/20
N2 - 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.
AB - 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.
KW - Glycemic Impact
KW - Recipe Classification
KW - Recipe Embeddings
UR - http://www.scopus.com/inward/record.url?scp=85076592943&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076592943&partnerID=8YFLogxK
U2 - 10.1145/3357729.3357748
DO - 10.1145/3357729.3357748
M3 - Conference contribution
AN - SCOPUS:85076592943
T3 - ACM International Conference Proceeding Series
SP - 31
EP - 35
BT - DPH 2019 - Proceedings of the 9th International Conference on Digital Public Health
PB - Association for Computing Machinery
T2 - 9th International Conference on Digital Public Health, DPH 2019
Y2 - 20 November 2019 through 23 November 2019
ER -