  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.
