Characterizing and Predicting Repeat Food Consumption Behavior for Just-in-Time Interventions
This work addresses the need for more effective food recommendation systems to promote healthy lifestyle changes, though it is incremental as it builds on existing algorithms.
The study tackled the problem of predicting daily food consumption habits to improve food recommender systems, finding that algorithms incorporating exploration-exploitation and temporal dynamics outperformed most state-of-the-art methods in next-day recommendation tasks.
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.