Extracting Food Substitutes From Food Diary via Distributional Similarity
This work addresses the need for healthier food recommendations by enabling the identification of food substitutes, though it is incremental as it applies an existing linguistic concept to a new domain.
The paper tackled the problem of identifying substitute relationships between food pairs from real-world consumption data, using a method inspired by distributional similarity in linguistics, and found it effective in identifying suitable substitutes based on evaluation with a crowdsourced dataset.
In this paper, we explore the problem of identifying substitute relationship between food pairs from real-world food consumption data as the first step towards the healthier food recommendation. Our method is inspired by the distributional hypothesis in linguistics. Specifically, we assume that foods that are consumed in similar contexts are more likely to be similar dietarily. For example, a turkey sandwich can be considered a suitable substitute for a chicken sandwich if both tend to be consumed with french fries and salad. To evaluate our method, we constructed a real-world food consumption dataset from MyFitnessPal's public food diary entries and obtained ground-truth human judgements of food substitutes from a crowdsourcing service. The experiment results suggest the effectiveness of the method in identifying suitable substitutes.