Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2019

Abstract

Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studied in food recommendation research. More importantly, the ability to predict daily eating habits of individuals is crucial to improve the effectiveness of food recommender systems in facilitating healthy lifestyle change. In this study, we analyze the patterns of repeat food consumptions using large-scale consumption data from a popular online fitness community called MyFitnessPal (MFP), conduct an offline evaluation of various state-of-the-art algorithms in predicting the next-day food consumption, and analyze their performance across different demographic groups and contexts. The experiment results show that algorithms incorporating the exploration-and-exploitation and temporal dynamics are more effective in the next-day recommendation task than most state-of-the-art algorithms.

Keywords

Food Recommendation, Implicit Feedback, Repeat Consumption

Discipline

Databases and Information Systems | Health Information Technology

Research Areas

Data Science and Engineering

Publication

DPH 2019: Proceedings of the 9th International Conference on Digital Public Health: Marseille, France, November 20-23

First Page

11

Last Page

20

ISBN

9781450372084

Identifier

10.1145/3357729.3357736

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors/LARC

Additional URL

https://doi.org/10.1145/3357729.3357736

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