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 - This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its International Research Centres in Singapore Funding Initiative.
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative.
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 -