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In this paper, we explore the problem of identifying substitute relationshipbetween food pairs from real-world food consumption data as the first steptowards the healthier food recommendation. Our method is inspired by thedistributional hypothesis in linguistics. Specifically, we assume that foodsthat are consumed in similar contexts are more likely to be similar dietarily.For example, a turkey sandwich can be considered a suitable substitute for achicken sandwich if both tend to be consumed with french fries and salad. Toevaluate our method, we constructed a real-world food consumption dataset fromMyFitnessPal's public food diary entries and obtained ground-truth humanjudgements of food substitutes from a crowdsourcing service. The experimentresults suggest the effectiveness of the method in identifying suitablesubstitutes.

Extracting Food Substitutes From Food Diary via Distributional Similarity