Investigating Substitutability of Food Items in Consumption Data

Food based dietary guidelines are insufficiently followed by consumers. One of the principal explanations of this failure is that they are too general and do not take into account eating habits. Providing personalized dietary recommendations via nutrition recommender system can hence help people improve their eating habits. Understanding eating habits is a keystone in order to build a context aware recommender system that delivers personalized dietary recommendations. As a first step towards this goal, we explore food relationships on real-world data using the INCA 2 dataset, a French consumption survey. We particularly focus on extracting food substitutions, i.e food items that can replace each other. We consider that two food items can be substituted if they are consumed during similar contexts. We define the context in the nutrition field and we introduce a measure of substitutability between food items based on consumption data that encodes the context.