Food Category Representatives: Extracting Categories from Meal Names in Food Recordings and Recipe Data

Abstract

Food Log is a multimedia recording tool for producing food records for many individuals. In one year of operation, Food Log has produced more than one million food records for meals eaten by users. We found nearly 70,000 unique food records among these data. In analyzing them, one of the challenges is to extract meal categories from such a large number of records. In this paper, we propose a method for compressing a meal name into a shorter representation. First, we collect similar meal names using a k-nearest neighbor search. Next, we construct a word graph to model the relationship between the meal names and items in the database. We select representative words by identifying minimal paths in the word graph. Finally, we obtain a few words that represent categorical information about the original meal name. We applied the method to data in food records for both Food Log and the Rakuten Recipe database. Our results show that the method worked effectively for both datasets.