Consumption Variety in Food Recommendation

Abstract

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 can observe large volumes of daily food logs of thousands of users. We first confirm that consumption variety is associated with lower overall calories consumed, higher vegetable consumption, and lower snack consumption. Motivated by these empirical patterns about variety and eating, we then seek out to design a novel multicriteria 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.