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 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.