Publication Type
Conference Paper
Version
submittedVersion
Publication Date
9-2016
Abstract
In this paper, we explore the problem of identifying substitute relationship between food pairs from real-world food consumption data as the first step towards the healthier food recommendation. Our method is inspired by the distributional hypothesis in linguistics. Specifically, we assume that foods that are consumed in similar contexts are more likely to be similar dietarily. For example, a turkey sandwich can be considered a suitable substitute for a chicken sandwich if both tend to be consumed with french fries and salad. To evaluate our method, we constructed a real-world food consumption dataset from MyFitnessPal's public food diary entries and obtained ground-truth human judgement of food substitutes from a crowdsourcing service. The ex- experiment results suggest the effectiveness of the method in identifying suitable substitutes.
Discipline
Computer Sciences | Databases and Information Systems
Publication
10th ACM Conference on Recommender Systems
Publisher
Singapore Management University
City or Country
Boston, USA
Citation
ACHANANUPARP, Palakorn and WEBER, Ingmar.
Extracting Food Substitutes From Food Diary via Distributional Similarity. (2016). 10th ACM Conference on Recommender Systems.
Available at: https://ink.library.smu.edu.sg/sis_research/3457
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.