Consumption Variety in Food Recommendation
51 Pages Posted: 4 Dec 2020 Last revised: 13 May 2021
Date Written: May 13, 2021
This research explores the justification and implications of incorporating consumption variety into mobile-based food recommendation systems. Our study makes use of data from a popular mobile fitness app, in which we are able to observe large volumes of daily food logs of thousands of users. We first confirm that consumption variety is associated with potential health benefits, such as lower overall calories consumed, higher vegetable consumption, and lower snack consumption. In light of these suggestive health benefits from consumption variety, we seek out to design a novel multi-criteria food recommendation system (FOODVAR) that can accommodate for variety in recommended foods. We then assess the impact of including this additional variety criterion in recommendation system performance, where we show that the incorporation of variety improves the algorithm's evaluation metrics.
Keywords: Behavioral Analytics; Behavior Tracking; Deep Learning; Eating Habits; Heterogeneity; Mobile Health and Wellness; Nutrition Science; Personalized Health; Recommendation System Design
JEL Classification: I1; I12; M31; C45
Suggested Citation: Suggested Citation