Name
Title | ||
|---|---|---|
Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine Learning |
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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357729.3357748 | Proceedings of the 9th International Conference on Digital Public Health |
Keywords | Field | DocType |
glycemic impact, recipe classification, recipe embeddings | Computer science,Crowdsourcing,Artificial intelligence,Glycemic,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-7208-4 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helena H. Lee | 1 | 0 | 0.34 |
| Palakorn Achananuparp | 2 | 302 | 23.16 |
| Yue Liu | 3 | 441 | 84.32 |
| Ee-Peng Lim | 4 | 5889 | 754.17 |
| Lav R. Varshney | 5 | 299 | 61.63 |